diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..72197be --- /dev/null +++ b/.env.example @@ -0,0 +1,9 @@ +# Qwen / DashScope credentials +# Fill ONE key variable only. Never commit the real value. +AWARELIQUID_LLM_API_KEY=填入你的千问_API_Key +# DASHSCOPE_API_KEY=填入你的千问_API_Key + +# Qwen OpenAI-compatible endpoint +AWARELIQUID_LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 +AWARELIQUID_LLM_MODEL=qwen-plus +AWARELIQUID_LLM_BACKEND=qwen diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..3a8d7c4 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,49 @@ +# 证据门禁（evidence gate）：单 job CI。 +# +# M1 仓库的治理缺口是"靠自觉、没有机器强制"——文档里声称的证据没人校验。 +# 本 job 把两件事变成合并/推送的硬性门槛： +# 1) python -m pytest -q 全量单元测试（离线、CPU-only）； +# 2) python scripts/check_results_refs.py 校验 RESULTS.md（单一事实源）里 +# 引用的每个路径真实存在、docs/runs/*/manifest.json 指向的仓库内产物存在、 +# 每个 *.sha256 sidecar 与实际文件哈希一致。 +# 任何一条失败，CI 红灯：杜绝"文档说了但仓库里没有"的断言进入 main。 +name: CI + +on: + push: + branches: [main] + pull_request: + +jobs: + test: + name: test (evidence gate) + runs-on: ubuntu-latest + timeout-minutes: 30 + permissions: + contents: read + env: + # 双保险：conftest.py 已在进程内固定 RNG/哈希行为，这里在 CI 环境层面 + # 再钉一次 PYTHONHASHSEED，保证任何子进程/工具脚本继承同样的种子。 + PYTHONHASHSEED: "0" + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Set up Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + # 先装 CPU 版 torch，避免 PyPI 默认 wheel 拖进 CUDA 运行时大包； + # 之后的 `pip install -e '.[dev]'` 检测到 torch 已满足 torch>=2.0.0，不会重装。 + - name: Install CPU-only PyTorch + run: pip install torch --index-url https://download.pytorch.org/whl/cpu + + - name: Install package with dev dependencies + run: pip install -e '.[dev]' + + - name: Run unit tests + run: python -m pytest -q + + - name: Check RESULTS.md evidence references + run: python scripts/check_results_refs.py diff --git a/README.md b/README.md index 0301c18..f0b623a 100644 --- a/README.md +++ b/README.md @@ -37,8 +37,8 @@ at — reading a short, relevant context and answering — and nothing else. |-------|--------|--------------| | **Chunk** | `adapter/chunker.py` | Split documents on sentence/paragraph boundaries into overlapping passages (language-agnostic, handles Chinese). | | **Embed** | `memory/encoder.py` | Lazy, L2-normalised multilingual sentence embedder (`intfloat/multilingual-e5-small` by default; `bge` selectable). | -| **Store** | `memory/knowledge_store.py` | SQLite-backed vector store (cosine) **plus an FTS5/BM25 lexical index**, content-addressable, with an anisotropy-robust centering option and an optional LRU cap. | -| **Retrieve** | `adapter/qa_agent.py` + `adapter/hybrid.py` | **Hybrid retrieval**: dense (e5 cosine) and lexical (BM25) channels fused with Reciprocal Rank Fusion, restricted to a caller-supplied document set. Dense captures meaning; BM25 captures exact tokens (rates, rating codes, `FY2023`). Falls back to dense-only when FTS5 is unavailable. | +| **Store** | `memory/lexical_store.py` | SQLite-backed FTS5/BM25 lexical index with deterministic source metadata and strict document filters. The legacy vector store remains research-only. | +| **Retrieve** | `adapter/qa_agent.py` + `adapter/evidence_index.py` | Competition-safe retrieval: structural source nodes, BM25, exact numeric/clause anchors, bounded neighbor expansion, and source-linked evidence deduplication. | | **Compress** | `adapter/compressor.py` | Extractive, **LLM-free** sentence selection under a character budget — keeps sentences by question overlap plus a salience bonus for numbers, %, currency and dates. Compression itself costs **zero** generation tokens. | | **Answer** | `adapter/qwen_client.py` | OpenAI-compatible Qwen chat call with exact per-call token accounting. | @@ -97,10 +97,9 @@ python examples/run_qa.py ``` Retrieval and compression are tunable via `RetrievalConfig` (chunk size, overlap, -`top_k`, compression budget, answer-token cap, hybrid-retrieval settings -(`hybrid`, `rrf_k`, `rrf_pool`, dense/sparse weights), and optional multi-query -retrieval (`multi_query`) that issues a sub-query per temporal operand and option -and unions the results — for cross-document comparison / computation questions). +`top_k`, compression budget, answer-token cap, structural evidence settings, and +optional multi-query retrieval). The formal `submit.py` entry point is lexical/BM25 +only; the legacy dense/hybrid path is not part of the competition workflow. ## Batch answering @@ -112,6 +111,12 @@ python submit.py --questions examples/sample_questions.jsonl \ --docs examples/sample_docs.json --out submission.csv ``` +Batch runs are atomic and ordered by default: after each completed question, +`.checkpoint.json` is replaced atomically. Re-running the command resumes +from the first uncommitted question and validates the question/document +fingerprints before continuing. Use `--checkpoint PATH` to choose the file, +`--fresh` to intentionally restart, or `--no-checkpoint` for an ephemeral run. + Questions are JSONL/JSON (`qid`, `question`, `options` as a `{letter: text}` dict or a list, `answer_format`, `doc_ids`); docs are a `{doc_id: text}` JSON or a directory of `.txt` files. To score a labelled set end-to-end (accuracy + @@ -127,11 +132,15 @@ run is always known. ## Tests ```bash -pytest +python3 -m venv .venv +.venv/bin/python -m pip install -e '.[dev]' +./scripts/test.sh ``` -The suite runs offline (mock backend, in-memory store) and covers chunking, -compression, answer parsing and the end-to-end agent loop. +The suite runs offline with the deterministic local mock backend and in-memory +stores; it does not require a Qwen API key or a GPU. The `lexical` retrieval +backend uses SQLite/FTS5 and is the only backend accepted by formal submission. +The original `hybrid` backend remains available for isolated research tests only. ## Validation @@ -144,18 +153,32 @@ python benchmarks/bench_adapter.py # real multilingual embedder python benchmarks/bench_adapter.py --fake # lexical stand-in, no model download ``` -On the bundled set (3 documents inflated to ≈2,800 tokens each, 6 questions): - -| Metric | Real e5 | Lexical stand-in | -|--------|:-------:|:----------------:| -| Retrieval recall@4 (answer chunk retrieved) | **6/6** | 5/6 | -| Answer-sentence retention after compression | **6/6** | 5/6 | -| Prompt context tokens vs. full-document | **≈1.1k vs 17k (−94%)** | −94% | -| Mean compression ratio | 0.48 | 0.46 | - -The semantic embedder recovers a paraphrased question ("归母净利润同比增长" vs. the -document's "归属于母公司股东的净利润…较上年同期增长") that pure lexical overlap misses — +On the bundled set (3 financial documents inflated with boilerplate to ≈22.7k +full-document tokens, 8 questions): + +| Metric | Lexical stand-in (8-q set, deterministic) | +|--------|:----------------:| +| Retrieval recall@1, dense → hybrid RRF | 6/8 → 6/8 | +| Retrieval recall@4 (answer chunk retrieved) | 7/8 → 8/8 | +| Answer-sentence retention after compression | 7/8 → 8/8 | +| End-to-end valid rows (formal format) | 8/8 | +| Prompt context tokens vs. full-document | 1254 vs 22668 (−94%) | + +These are **single-run screening counts, not conclusions**: they prove the +pipeline works and the gate conditions hold, not that retrieval beats any +baseline. Citing them externally requires multi-seed expansion through the +`publishable()` gate in `benchmarks/experiment_protocol.py`. Numbers are +reconciled in `docs/RESULTS.md` (single source of truth); pre-registration +rules live in `docs/PREREGISTRATION.md`. The `--fake` run is byte-identical +across processes (three-run proof: `benchmarks/results/bench_adapter_fake_20260906_r1.log` +and its `r2`/`r3` siblings; pinned by `tests/test_bench_determinism.py`). + +A historical single-run e5 result (6-question set) is archived in +`docs/RESULTS.md` (SUPERSEDED section): it showed the multilingual embedder +recovering a paraphrased question ("归母净利润同比增长" vs. the document's +"归属于母公司股东的净利润…较上年同期增长") that pure lexical overlap misses — which is why the multilingual model, not a keyword index, drives retrieval. +Re-run it through the `publishable()` gate before citing it anywhere. **Scope:** this benchmark validates retrieval, compression and token efficiency, which are the adapter's job. The final letter is chosen by the frozen model, so diff --git a/awareliquid/__init__.py b/awareliquid/__init__.py index 841cbe5..6782eb0 100644 --- a/awareliquid/__init__.py +++ b/awareliquid/__init__.py @@ -33,6 +33,7 @@ summarize_usage, ) from .memory import ( + LexicalKnowledgeMemory, PersistentKnowledgeMemory, SentenceEncoder, ) @@ -47,6 +48,7 @@ "summarize_usage", "build_chat_client", "PersistentKnowledgeMemory", + "LexicalKnowledgeMemory", "SentenceEncoder", "__version__", ] diff --git a/awareliquid/adapter/__init__.py b/awareliquid/adapter/__init__.py index c5f18e0..60b8c05 100644 --- a/awareliquid/adapter/__init__.py +++ b/awareliquid/adapter/__init__.py @@ -12,10 +12,12 @@ MockChatClient, QwenChatClient, TokenUsage, + UsageLedger, build_chat_client, ) from .chunker import Chunk, chunk_document from .compressor import CompressedContext, ExtractiveCompressor +from .evidence_index import EvidenceNode, build_evidence_nodes from .hybrid import rrf_fuse from .schemas import AnswerResult, parse_answer, summarize_usage @@ -27,9 +29,12 @@ "MockChatClient", "ChatResult", "TokenUsage", + "UsageLedger", "build_chat_client", "Chunk", "chunk_document", + "EvidenceNode", + "build_evidence_nodes", "ExtractiveCompressor", "CompressedContext", "AnswerResult", diff --git a/awareliquid/adapter/afac_contract.py b/awareliquid/adapter/afac_contract.py new file mode 100644 index 0000000..fce6534 --- /dev/null +++ b/awareliquid/adapter/afac_contract.py @@ -0,0 +1,243 @@ +"""Fail-closed AFAC question and submission CSV contract. + +This module is deliberately pure: it performs no model invocation, network access, +or file I/O. The official CSV is defined here rather than inferred from legacy +submission examples. +""" + +from __future__ import annotations + +import csv +import io +from dataclasses import dataclass +from typing import Any, Iterable, Mapping, Optional, Sequence, Tuple + + +CSV_HEADER = ("qid", "answer", "prompt_tokens", "completion_tokens", "total_tokens") +OPTION_KEYS = ("A", "B", "C", "D") +ANSWER_FORMATS = ("mcq", "multi", "tf") +TOKEN_BUDGET = 5_000_000 +_MISSING = object() + + +class ContractError(ValueError): + """Structured error raised instead of repairing malformed formal data.""" + + def __init__(self, code: str, field: str, message: str) -> None: + super().__init__(message) + self.code = code + self.field = field + self.message = message + + def as_dict(self) -> dict[str, dict[str, str]]: + return {"error": {"code": self.code, "field": self.field, "message": self.message}} + + +@dataclass(frozen=True) +class ParsedQuestion: + qid: str + domain: str + split: str + question: str + options: Mapping[str, str] + answer_format: str + type_hint: str + doc_ids: Optional[Tuple[str, ...]] + + def to_dict(self) -> dict[str, Any]: + return { + "qid": self.qid, + "domain": self.domain, + "split": self.split, + "question": self.question, + "options": dict(self.options), + "answer_format": self.answer_format, + "type": self.type_hint, + "doc_ids": list(self.doc_ids) if self.doc_ids is not None else None, + } + + +@dataclass(frozen=True) +class SubmissionAnswer: + """One answer and its complete, known model usage.""" + + qid: str + answer: str + prompt_tokens: int + completion_tokens: int + total_tokens: int + + def __post_init__(self) -> None: + if not isinstance(self.qid, str) or not self.qid.strip() or self.qid == "summary": + raise ContractError("INVALID_QID", "qid", "qid must be a non-empty non-summary string") + if not isinstance(self.answer, str): + raise ContractError("INVALID_FORMAL_ANSWER", "answer", "answer must be a string") + _validate_usage(self.prompt_tokens, self.completion_tokens, self.total_tokens, "tokens") + + +@dataclass(frozen=True) +class SubmissionSummary: + prompt_tokens: int + completion_tokens: int + total_tokens: int + + def __post_init__(self) -> None: + _validate_usage( + self.prompt_tokens, self.completion_tokens, self.total_tokens, "summary" + ) + if self.total_tokens > TOKEN_BUDGET: + raise ContractError( + "TOKEN_BUDGET_EXCEEDED", + "summary.total_tokens", + f"total tokens exceed the {TOKEN_BUDGET:,} token budget", + ) + + +def _validate_usage(prompt_tokens: Any, completion_tokens: Any, total_tokens: Any, field: str) -> None: + values = (prompt_tokens, completion_tokens, total_tokens) + if any(isinstance(value, bool) or not isinstance(value, int) or value < 0 for value in values): + raise ContractError("UNKNOWN_OR_INVALID_TOKEN_USAGE", field, "token usage must be known non-negative integers") + if total_tokens != prompt_tokens + completion_tokens: + raise ContractError("INCONSISTENT_TOKEN_TOTAL", field, "total_tokens must equal prompt_tokens plus completion_tokens") + + +def _text(payload: Mapping[str, Any], field: str) -> str: + value = payload.get(field) + if not isinstance(value, str) or not value.strip(): + raise ContractError("INVALID_REQUIRED_FIELD", field, f"{field} must be a non-empty string") + return value + + +def _options(value: Any, answer_format: str) -> dict[str, str]: + allowed_keys = {"A", "B"} if answer_format == "tf" else set(OPTION_KEYS) + if not isinstance(value, Mapping) or set(value) != allowed_keys: + expected = "A and B" if answer_format == "tf" else "A, B, C, and D" + raise ContractError("INVALID_OPTIONS", "options", f"options must contain exactly {expected}") + keys = ("A", "B") if answer_format == "tf" else OPTION_KEYS + if any(not isinstance(value[key], str) or not value[key].strip() for key in keys): + raise ContractError("INVALID_OPTIONS", "options", "option values must be non-empty strings") + return {key: value[key] for key in keys} + + +def _doc_ids(payload: Mapping[str, Any], split: str) -> Optional[Tuple[str, ...]]: + value = payload.get("doc_ids", _MISSING) + if split == "A": + if value is _MISSING or value is None: + raise ContractError("DOC_IDS_REQUIRED_FOR_A", "doc_ids", "split A requires doc_ids") + if not isinstance(value, list) or not value: + raise ContractError("INVALID_DOC_IDS", "doc_ids", "doc_ids must be a non-empty list of strings") + if any(not isinstance(item, str) or not item.strip() for item in value): + raise ContractError("INVALID_DOC_IDS", "doc_ids", "doc_ids must be a non-empty list of strings") + return tuple(value) + + if value is _MISSING or value is None: + return None + raise ContractError("SPLIT_DOC_IDS_CONFLICT", "doc_ids", "split B must not include doc_ids") + + +def parse_question(payload: Mapping[str, Any]) -> ParsedQuestion: + """Validate one official question; split and answer format are never inferred.""" + + if not isinstance(payload, Mapping): + raise ContractError("INVALID_QUESTION", "question", "question must be an object") + split_value = payload.get("split") + if not isinstance(split_value, str) or split_value.upper() not in {"A", "B"}: + raise ContractError("INVALID_SPLIT", "split", "split must be A or B") + answer_format = payload.get("answer_format") + if answer_format not in ANSWER_FORMATS: + raise ContractError("INVALID_ANSWER_FORMAT", "answer_format", "answer_format must be mcq, multi, or tf") + split = split_value.upper() + return ParsedQuestion( + qid=_text(payload, "qid"), + domain=_text(payload, "domain"), + split=split, + question=_text(payload, "question"), + options=_options(payload.get("options"), answer_format), + answer_format=answer_format, + type_hint=_text(payload, "type"), + doc_ids=_doc_ids(payload, split), + ) + + +def parse_questions(payloads: Iterable[Mapping[str, Any]]) -> tuple[ParsedQuestion, ...]: + """Validate a complete question collection and reject duplicate qids.""" + + if isinstance(payloads, (str, bytes)): + raise ContractError("INVALID_QUESTIONS", "questions", "questions must be an iterable of objects") + try: + questions = tuple(parse_question(payload) for payload in payloads) + except TypeError as error: + raise ContractError("INVALID_QUESTIONS", "questions", "questions must be an iterable of objects") from error + qids = [question.qid for question in questions] + if len(qids) != len(set(qids)): + raise ContractError("DUPLICATE_QID", "qid", "question qids must be unique") + return questions + + +def _validate_answer_for_question(answer: SubmissionAnswer, question: ParsedQuestion) -> None: + value = answer.answer + if question.answer_format == "tf": + if value not in {"A", "B"}: + raise ContractError("INVALID_FORMAL_ANSWER", "answer", "tf answer must be A or B") + return + if question.answer_format == "mcq": + if value not in question.options: + raise ContractError("INVALID_FORMAL_ANSWER", "answer", "answer must be an uppercase provided option letter") + return + if ( + not value + or any(letter not in question.options for letter in value) + or value != "".join(sorted(set(value))) + ): + raise ContractError( + "INVALID_FORMAL_ANSWER", + "answer", + "multi answer must be sorted, deduplicated uppercase option letters without separators", + ) + + +def validate_submission( + questions: Sequence[ParsedQuestion], answers: Sequence[SubmissionAnswer] +) -> SubmissionSummary: + """Validate completeness, formal answers, aggregate token equality, and budget.""" + + question_qids = [question.qid for question in questions] + answer_qids = [answer.qid for answer in answers] + if len(question_qids) != len(set(question_qids)): + raise ContractError("DUPLICATE_QID", "qid", "question qids must be unique") + if len(answer_qids) != len(set(answer_qids)): + raise ContractError("DUPLICATE_QID", "qid", "output qids must be unique") + expected = set(question_qids) + actual = set(answer_qids) + if actual != expected: + missing = sorted(expected - actual) + unknown = sorted(actual - expected) + detail = "missing " + repr(missing) if missing else "unknown " + repr(unknown) + raise ContractError("INCOMPLETE_OR_UNKNOWN_QIDS", "qid", f"output qids must exactly match inputs: {detail}") + + by_qid = {question.qid: question for question in questions} + for answer in answers: + _validate_answer_for_question(answer, by_qid[answer.qid]) + + return SubmissionSummary( + prompt_tokens=sum(answer.prompt_tokens for answer in answers), + completion_tokens=sum(answer.completion_tokens for answer in answers), + total_tokens=sum(answer.total_tokens for answer in answers), + ) + + +def render_submission_csv( + questions: Sequence[ParsedQuestion], answers: Sequence[SubmissionAnswer] +) -> str: + """Return the official UTF-8-text five-column CSV after full validation.""" + + summary = validate_submission(questions, answers) + rows: list[tuple[Any, ...]] = [CSV_HEADER] + rows.append(("summary", "", summary.prompt_tokens, summary.completion_tokens, summary.total_tokens)) + rows.extend( + (answer.qid, answer.answer, answer.prompt_tokens, answer.completion_tokens, answer.total_tokens) + for answer in answers + ) + output = io.StringIO(newline="") + csv.writer(output, lineterminator="\n").writerows(rows) + return output.getvalue() diff --git a/awareliquid/adapter/compressor.py b/awareliquid/adapter/compressor.py index 50c9af1..66dc388 100644 --- a/awareliquid/adapter/compressor.py +++ b/awareliquid/adapter/compressor.py @@ -65,11 +65,17 @@ class CompressedContext: class ExtractiveCompressor: """Local, LLM-free sentence selection under a character budget.""" - def __init__(self, budget_chars: int = 1200, salience_weight: float = 0.5): + def __init__( + self, + budget_chars: int = 1200, + salience_weight: float = 0.5, + ensure_passage_coverage: bool = True, + ): if budget_chars <= 0: raise ValueError(f"budget_chars must be positive, got {budget_chars}") self.budget_chars = int(budget_chars) self.salience_weight = float(salience_weight) + self.ensure_passage_coverage = bool(ensure_passage_coverage) def _score_sentence(self, sent: str, q_terms: set) -> float: s_terms = _terms(sent) @@ -87,28 +93,101 @@ def _score_sentence(self, sent: str, q_terms: set) -> float: salience = self.salience_weight if _SALIENT.search(sent) else 0.0 return length_norm + salience - def compress(self, question: str, passages: Sequence[str]) -> CompressedContext: + def compress( + self, + question: str, + passages: Sequence[str], + coverage_queries: Sequence[str] | None = None, + ) -> CompressedContext: """Select the most relevant sentences from *passages* under the budget.""" q_terms = _terms(question) # (original_index, sentence, score) across all passages, in reading order. - indexed: List[Tuple[int, str, float]] = [] - for passage in passages: + indexed: List[Tuple[int, int, str, float]] = [] + for passage_idx, passage in enumerate(passages): for sent in (p.strip() for p in _SENT_SPLIT.split(passage or "") if p.strip()): idx = len(indexed) - indexed.append((idx, sent, self._score_sentence(sent, q_terms))) + indexed.append((idx, passage_idx, sent, self._score_sentence(sent, q_terms))) if not indexed: return CompressedContext(text="", kept_sentences=0, source_sentences=0) # Greedily take the highest-scoring sentences until the budget is spent. - ranked = sorted(indexed, key=lambda t: t[2], reverse=True) + ranked = sorted(indexed, key=lambda t: (t[3], -t[0]), reverse=True) chosen, used = [], 0 - for idx, sent, score in ranked: + + # A globally ranked sentence list can spend the entire budget on one + # highly lexical passage. That is especially harmful for comparison + # and multi-choice questions, where each retrieved passage may contain + # a different option's evidence. Reserve one best sentence per + # passage first, then use the remaining budget for normal relevance + # ranking. This is document-agnostic and does not depend on answer + # labels or a particular benchmark domain. + reserved = [] + if self.ensure_passage_coverage: + best_by_passage = {} + for item in indexed: + _idx, passage_idx, _sent, score = item + current = best_by_passage.get(passage_idx) + if current is None or (score, -item[0]) > (current[3], -current[0]): + best_by_passage[passage_idx] = item + reserved = sorted(best_by_passage.values(), key=lambda t: t[0]) + + selected = set() + + # Reserve evidence for each independent target (normally the question + # and each answer option). A single enriched query can rank one option + # highly and starve the other options of context. The queries are + # caller-provided text only; no answer labels or benchmark-specific + # rules are involved. + coverage_terms = [_terms(query) for query in (coverage_queries or (question,))] + for query_terms in coverage_terms: + if not query_terms: + continue + candidates = [ + item for item in indexed + if item[2] not in selected + ] + if not candidates: + break + best = max( + candidates, + key=lambda item: ( + self._score_sentence(item[2], query_terms), + -item[0], + ), + ) + idx, _passage_idx, sent, _original_score = best + score = self._score_sentence(sent, query_terms) + if score <= 0 and chosen: + continue + if used + len(sent) > self.budget_chars and chosen: + continue + chosen.append((idx, sent)) + selected.add(sent) + used += len(sent) + if used >= self.budget_chars: + break + + for idx, passage_idx, sent, score in reserved: + if sent in selected: + continue + if used + len(sent) > self.budget_chars and chosen: + continue + chosen.append((idx, sent)) + selected.add(sent) + used += len(sent) + if used >= self.budget_chars: + break + + for idx, passage_idx, sent, score in ranked: + if sent in selected: + continue if score <= 0 and chosen: break # nothing relevant left to add if used + len(sent) > self.budget_chars and chosen: continue chosen.append((idx, sent)) + selected.add(sent) used += len(sent) if used >= self.budget_chars: break diff --git a/awareliquid/adapter/contracts.py b/awareliquid/adapter/contracts.py new file mode 100644 index 0000000..09255ae --- /dev/null +++ b/awareliquid/adapter/contracts.py @@ -0,0 +1,243 @@ +"""Frozen AFAC input, internal, and formal-output contracts.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Mapping, Optional, Tuple + + +AFAC_TOKEN_BUDGET = 5_000_000 +ANSWER_FORMATS = ("mcq", "multi", "tf") +OPTION_KEYS = ("A", "B", "C", "D") +TF_OUTPUTS = ("T", "F") +_MISSING = object() + + +class ContractError(ValueError): + """Machine-readable fail-closed contract violation.""" + + def __init__( + self, + code: str, + field: str, + detail: str, + *, + split: Optional[str] = None, + ) -> None: + super().__init__(detail) + self.code = code + self.field = field + self.detail = detail + self.split = split + + def as_dict(self) -> dict[str, Optional[str]]: + return { + "code": self.code, + "field": self.field, + "detail": self.detail, + "split": self.split, + } + + +@dataclass(frozen=True) +class CompetitionQuestion: + """Internal standard object after input-contract validation.""" + + qid: str + domain: str + split: str + question: str + options: Mapping[str, str] + answer_format: str + type_hint: str + doc_ids: Optional[Tuple[str, ...]] + + +@dataclass(frozen=True) +class SubmissionTokenUsage: + """Per-question model-interface token columns in formal CSV order.""" + + prompt_tokens: int + completion_tokens: int + total_tokens: int + + def __post_init__(self) -> None: + values = (self.prompt_tokens, self.completion_tokens, self.total_tokens) + if any(isinstance(value, bool) or not isinstance(value, int) for value in values): + raise ContractError( + "INVALID_TOKEN_USAGE", "tokens", "token values must be integers" + ) + if any(value < 0 for value in values): + raise ContractError( + "INVALID_TOKEN_USAGE", "tokens", "token values cannot be negative" + ) + if self.total_tokens != self.prompt_tokens + self.completion_tokens: + raise ContractError( + "INVALID_TOKEN_USAGE", + "total_tokens", + "total_tokens must equal prompt_tokens + completion_tokens", + ) + + +def _required_text(payload: Mapping[str, Any], field: str) -> str: + value = payload.get(field) + if not isinstance(value, str) or not value.strip(): + raise ContractError( + "INVALID_REQUIRED_FIELD", field, f"{field} must be a non-empty string" + ) + return value + + +def _validate_options(value: Any, answer_format: str) -> dict[str, str]: + # Audited real TF inputs use A/B labels; mcq and multi remain four-option. + allowed_keys = {"A", "B"} if answer_format == "tf" else set(OPTION_KEYS) + value_keys = set(value) if isinstance(value, Mapping) else None + if value_keys is None or (value_keys != allowed_keys and value_keys != set(OPTION_KEYS)): + expected = "A and B (or A, B, C, and D)" if answer_format == "tf" else "A, B, C, and D" + raise ContractError( + "INVALID_OPTIONS", + "options", + f"options must be an object with exactly {expected} keys", + ) + keys = OPTION_KEYS if value_keys == set(OPTION_KEYS) else ("A", "B") + if any(not isinstance(value[key], str) for key in keys): + raise ContractError( + "INVALID_OPTIONS", "options", "every option value must be a string" + ) + return {key: value[key] for key in keys} + + +def _validate_doc_ids(payload: Mapping[str, Any], split: str) -> Optional[Tuple[str, ...]]: + doc_ids = payload.get("doc_ids", _MISSING) + if split == "A": + is_valid = ( + isinstance(doc_ids, list) + and bool(doc_ids) + and all(isinstance(doc_id, str) and bool(doc_id.strip()) for doc_id in doc_ids) + ) + if not is_valid: + raise ContractError( + "INVALID_DOC_IDS_FOR_SPLIT_A", + "doc_ids", + "split A requires a non-empty list of non-empty document IDs", + split=split, + ) + return tuple(doc_ids) + + if doc_ids is _MISSING or doc_ids is None: + return None + if isinstance(doc_ids, list): + raise ContractError( + "DOC_IDS_SPLIT_CONFLICT", + "doc_ids", + "split B does not accept a doc_ids list, including an empty list", + split=split, + ) + raise ContractError( + "INVALID_DOC_IDS_FOR_SPLIT_B", + "doc_ids", + "split B accepts only a missing or null doc_ids field", + split=split, + ) + + +def validate_question(payload: Mapping[str, Any]) -> CompetitionQuestion: + """Validate one official input object without inferring its split.""" + + if not isinstance(payload, Mapping): + raise ContractError("INVALID_QUESTION", "question", "question must be an object") + if "split" not in payload: + raise ContractError("MISSING_SPLIT", "split", "split is required and is not inferred") + split_value = payload["split"] + if not isinstance(split_value, str) or split_value.upper() not in {"A", "B"}: + raise ContractError("INVALID_SPLIT", "split", "split must be A or B") + split = split_value.upper() + + answer_format = payload.get("answer_format") + if answer_format not in ANSWER_FORMATS: + raise ContractError( + "INVALID_ANSWER_FORMAT", + "answer_format", + "answer_format must be mcq, multi, or tf", + split=split, + ) + + return CompetitionQuestion( + qid=_required_text(payload, "qid"), + domain=_required_text(payload, "domain"), + split=split, + question=_required_text(payload, "question"), + options=_validate_options(payload.get("options"), answer_format), + answer_format=answer_format, + type_hint=_required_text(payload, "type"), + doc_ids=_validate_doc_ids(payload, split), + ) + + +def validate_formal_answer(answer_format: str, answer: str) -> str: + """Validate the answer representation used in the formal CSV.""" + + is_valid = False + if answer_format == "mcq": + is_valid = answer in OPTION_KEYS + elif answer_format == "tf": + is_valid = answer in TF_OUTPUTS + elif answer_format == "multi" and isinstance(answer, str): + is_valid = bool(answer) and all(char in OPTION_KEYS for char in answer) + is_valid = is_valid and answer == "".join(sorted(set(answer))) + if not is_valid: + raise ContractError( + "INVALID_FORMAL_ANSWER", + "answer", + f"invalid {answer_format!r} formal answer {answer!r}", + ) + return answer + + +def build_summary_row( + budget_tokens: int, + used_tokens: int, + unused_tokens: Optional[int] = None, +) -> list[Any]: + """Build a structural summary row without deriving its unused field.""" + + if any( + isinstance(value, bool) or not isinstance(value, int) or value < 0 + for value in (budget_tokens, used_tokens) + ) or used_tokens > budget_tokens: + raise ContractError( + "INVALID_TOKEN_USAGE", + "used_tokens", + "used_tokens and token_budget must be non-negative integers with used <= budget", + ) + if unused_tokens is None: + raise ContractError( + "MISSING_UNUSED_TOKENS", + "unused_tokens", + "unused_tokens must be supplied explicitly before rendering", + ) + if isinstance(unused_tokens, bool) or not isinstance(unused_tokens, int) or unused_tokens < 0: + raise ContractError( + "INVALID_TOKEN_USAGE", + "unused_tokens", + "unused_tokens must be a non-negative integer", + ) + return ["summary", budget_tokens, used_tokens, unused_tokens] + + +def build_question_row( + qid: str, answer: str, usage: SubmissionTokenUsage +) -> list[Any]: + """Build one formal per-question row after its answer has been validated.""" + + if not isinstance(qid, str) or not qid: + raise ContractError("INVALID_REQUIRED_FIELD", "qid", "qid cannot be empty") + if not isinstance(answer, str) or not answer: + raise ContractError("INVALID_FORMAL_ANSWER", "answer", "answer cannot be empty") + return [ + qid, + answer, + usage.prompt_tokens, + usage.completion_tokens, + usage.total_tokens, + ] diff --git a/awareliquid/adapter/evidence_index.py b/awareliquid/adapter/evidence_index.py new file mode 100644 index 0000000..e9dff86 --- /dev/null +++ b/awareliquid/adapter/evidence_index.py @@ -0,0 +1,155 @@ +"""Deterministic document structure for competition-safe retrieval. + +The index stores navigation metadata and exact source text only. It does not +produce semantic summaries, embeddings, or answer-bearing conclusions. +""" + +from __future__ import annotations + +from dataclasses import dataclass +import re +from typing import List, Optional, Tuple + +from .chunker import chunk_document + + +_ANCHOR = re.compile( + r"(?:19|20)\d{2}\s*年?" + r"|\d+(?:,\d{3})*(?:\.\d+)?\s*" + r"(?:个百分点|亿元|万元|美元|港元|bp|BP|%|％|亿|万|元)" + r"|第\s*[一二三四五六七八九十百\d]+\s*" + r"(?:条|章|节|款|项|个保单年度|保单年度)" + r"|AAA|AA\+?" + r"|除外|例外|不适用|特殊情形|免责|等待期" +) +_CLAUSE = re.compile( + r"第\s*[一二三四五六七八九十百\d]+\s*(?:条|章|节|款|项)" + r"|(? Tuple[str, ...]: + seen = set() + result = [] + for value in values: + normalized = re.sub(r"\s+", "", value).lower() + if normalized and normalized not in seen: + seen.add(normalized) + result.append(value.strip()) + return tuple(result) + + +def _heading(text: str) -> Optional[str]: + for line in reversed(text.splitlines()): + candidate = line.strip() + if candidate and _HEADING.match(candidate) and len(candidate) <= 160: + return candidate + return None + + +def _table_fields(content: str) -> Tuple[Optional[str], Tuple[str, ...]]: + rows = [ + [part.strip() for part in line.split("|") if part.strip()] + for line in (content or "").splitlines() + if "|" in line + ] + if not rows: + return None, () + row_labels = [row[0] for row in rows if row] + row_id = " ".join(row_labels) if row_labels else None + column_ids = tuple(rows[0][1:]) if len(rows) > 1 else () + return row_id, column_ids + + +def _page_chunks(doc_id: str, text: str, max_chars: int, overlap_chars: int): + pages = (text or "").split("\f") + chunks = [] + for page, page_text in enumerate(pages, start=1): + for chunk in chunk_document( + doc_id, page_text, max_chars=max_chars, overlap_chars=overlap_chars + ): + chunks.append((page, page_text, chunk.text)) + return chunks + + +@dataclass(frozen=True) +class EvidenceNode: + """One exact source span plus deterministic navigation fields.""" + + doc_id: str + node_id: str + chunk_idx: int + page: int + content: str + section: Optional[str] + clause_id: Optional[str] + table_id: Optional[str] + anchors: Tuple[str, ...] + neighbor_chunk_idxs: Tuple[int, ...] + row_id: Optional[str] = None + column_ids: Tuple[str, ...] = () + + def as_meta(self) -> dict: + return { + "doc_id": self.doc_id, + "node_id": self.node_id, + "chunk_idx": self.chunk_idx, + "page": self.page, + "section": self.section, + "clause_id": self.clause_id, + "table_id": self.table_id, + "row_id": self.row_id, + "column_ids": list(self.column_ids), + "anchors": list(self.anchors), + "neighbor_chunk_idxs": list(self.neighbor_chunk_idxs), + "search_terms": " ".join( + value + for value in (self.section, self.clause_id, *self.anchors) + if value + ), + } + + +def build_evidence_nodes( + doc_id: str, + text: str, + max_chars: int = 800, + overlap_chars: int = 120, +) -> List[EvidenceNode]: + """Build a source-linked, non-semantic index from document text.""" + raw_chunks = _page_chunks(doc_id, text, max_chars, overlap_chars) + nodes = [] + for chunk_idx, (page, page_text, content) in enumerate(raw_chunks): + anchors = _unique(_ANCHOR.findall(content)) + clauses = _CLAUSE.findall(content) + section = _heading(content) or _heading(page_text) + clause_id = clauses[0].strip() if clauses else None + table_id = f"{doc_id}:page-{page}" if "|" in content else None + row_id, column_ids = _table_fields(content) + neighbors = tuple( + index + for index in (chunk_idx - 1, chunk_idx + 1) + if 0 <= index < len(raw_chunks) + ) + nodes.append( + EvidenceNode( + doc_id=str(doc_id), + node_id=f"{doc_id}:node-{chunk_idx}", + chunk_idx=chunk_idx, + page=page, + content=content, + section=section, + clause_id=clause_id, + table_id=table_id, + row_id=row_id, + column_ids=column_ids, + anchors=anchors, + neighbor_chunk_idxs=neighbors, + ) + ) + return nodes diff --git a/awareliquid/adapter/qa_agent.py b/awareliquid/adapter/qa_agent.py index 5b8ad04..bc7b04c 100644 --- a/awareliquid/adapter/qa_agent.py +++ b/awareliquid/adapter/qa_agent.py @@ -2,7 +2,7 @@ ``MemoryQAAgent`` wires the pieces together into the external adapter loop: - ingest document text -> chunks -> embeddings -> vector store + ingest document text -> chunks -> lexical/vector store answer question -> retrieve chunks -> compress -> compact prompt -> Qwen chat API -> parsed answer + token usage @@ -16,20 +16,201 @@ import re from dataclasses import dataclass -from typing import Dict, List, Optional, Sequence, Tuple +from decimal import Decimal, InvalidOperation +import json +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence, Tuple -from ..memory.encoder import SentenceEncoder -from ..memory.knowledge_store import PersistentKnowledgeMemory -from .chunker import chunk_document from .compressor import ExtractiveCompressor +from .evidence_index import build_evidence_nodes from .hybrid import rrf_fuse -from .qwen_client import ChatResult, build_chat_client +from .qwen_client import ( + ChatResult, + DEFAULT_TOKEN_BUDGET, + QwenChatClient, + TokenUsage, + build_chat_client, +) from .schemas import AnswerResult, parse_answer # Temporal anchors that split a comparison/computation question into operands: # a year ("2023" / "2023 年"), or a quarter ("Q3", "第三季度", "三季度"/"上半年"). _YEAR = re.compile(r"(?:19|20)\d{2}") _QUARTER = re.compile(r"Q[1-4]|第[一二三四1-4]季度|[上下]半年|[一二三四1-4]季度") +_CLAIM_RELATION = re.compile( + r"高于|低于|超过|不超过|少于|大于|小于|同比|增长|下降|增幅|复合增速|" + r"占比|比例|每股|每10股|倍|差额|分别|排序|高到低|低到高|升序|降序" +) +_CALCULATION_MARKER = re.compile( + r"计算|排序|高到低|低到高|升序|降序|合计|总计|平均|金额|现金价值|退保所得|" + r"每股|每10股" +) +_OPTION_STATE = re.compile( + r"^\s*([A-Z])\s*=\s*(SUPPORTED|REFUTED|INSUFFICIENT)\b", + re.IGNORECASE, +) +_OPTION_RELATION_STATE = re.compile( + r"^\s*([A-Z])\s*:\s*.*?\bstate\s*=\s*" + r"(SUPPORTED|REFUTED|INSUFFICIENT)\b", + re.IGNORECASE, +) +_ANSWER_LINE = re.compile(r"(?:^|\n)\s*ANSWER\s*:\s*([A-Z]+)\b", re.IGNORECASE) +_EVIDENCE_SLOT_GROUPS = ( + ("营业收入", "营业总收入", "主营业务收入"), + ("归母净利润", "归属于上市公司股东的净利润"), + ("经营现金流", "经营活动产生的现金流量净额", "经营活动现金流量净额"), + ("研发投入", "研发费用", "研发投入占营业收入比例"), + ("现金分红", "每10股现金分红", "利润分配方案"), + ("一般医疗", "一般医疗保险金", "住院医疗费用"), + ("免赔额", "共享免赔额", "家庭共享免赔额"), + ("施救费用", "施救费", "必要合理施救费用"), + ("除外责任", "免责", "不承担责任"), +) +_EVIDENCE_ANCHOR = re.compile( + r"(?:19|20)\d{2}\s*年?" + r"|\d+(?:,\d{3})*(?:\.\d+)?\s*(?:个百分点|亿元|万元|美元|港元|bp|BP|%|％|亿|万|元)" + r"|第\s*[一二三四五六七八九十百\d]+\s*(?:条|章|节|款|项|个保单年度|保单年度)" + r"|AAA|AA\+?" + r"|除外|例外|但|不适用|特殊情形|免责|等待期" + , +) +def _normalise_evidence_anchor(value: str) -> str: + return re.sub(r"\s+", "", str(value)).lower() + + +def _missing_evidence_anchors(question: str, option: str, evidence: str) -> List[str]: + """Find exact, high-signal anchors from a claim missing in its evidence.""" + anchors: List[str] = [] + seen = set() + # Option anchors are the primary coverage contract. Question anchors are + # added only when they are structural facts (dates, amounts, clauses, ...), + # not ordinary connective words such as ``但``. This keeps a supplement + # query targeted instead of turning every multi-choice question into a + # broad second retrieval pass. + for match in _EVIDENCE_ANCHOR.finditer(f"{option} {question}"): + anchor = match.group(0).strip() + key = _normalise_evidence_anchor(anchor) + if key and key not in seen and len(key) > 1: + seen.add(key) + anchors.append(anchor) + evidence_text = _normalise_evidence_anchor(evidence) + return [anchor for anchor in anchors if _normalise_evidence_anchor(anchor) not in evidence_text] + + +def _missing_evidence_slots(question: str, option: str, evidence: str) -> List[str]: + source = f"{question} {option}" + normalized = _normalise_evidence_anchor(evidence) + missing = [] + for group in _EVIDENCE_SLOT_GROUPS: + if not any(term in source for term in group): + continue + if any(_normalise_evidence_anchor(term) in normalized for term in group): + continue + missing.append(" ".join(group)) + return missing + + +def _verify_calculation_draft(raw: str) -> str: + """Validate a Qwen calculation ledger with local Decimal arithmetic. + + The model may extract facts and propose derived values, but it is not + trusted to perform the arithmetic. Invalid or incomplete ledgers are + discarded instead of being repeated as authoritative evidence. + """ + text = (raw or "").strip() + if not text: + return "" + try: + payload = json.loads(text) + except (json.JSONDecodeError, TypeError): + return "" + if not isinstance(payload, dict): + return "" + + def parse_decimal(value): + if isinstance(value, bool) or value is None: + raise ValueError("invalid numeric value") + text = str(value).strip().replace(",", "") + if text.endswith("%"): + return Decimal(text[:-1]) / Decimal("100") + return Decimal(text) + + values = {} + rendered = [] + for item in payload.get("facts", []): + if not isinstance(item, dict): + continue + name = item.get("name") + value = item.get("value") + if not isinstance(name, str) or not name.strip(): + continue + try: + parsed = parse_decimal(value) + except (InvalidOperation, ValueError, TypeError): + continue + if not parsed.is_finite(): + continue + key = name.strip() + values[key] = parsed + unit = item.get("unit") or "" + rendered.append(f"- fact {key} = {parsed}{unit}") + + operations = {"add", "subtract", "multiply", "divide"} + pending = [item for item in payload.get("derived", []) if isinstance(item, dict)] + while pending: + progressed = False + remaining = [] + for item in pending: + name = item.get("name") + operation = item.get("operation") + operands = item.get("operands") + reported = item.get("value") + if ( + not isinstance(name, str) + or not name.strip() + or operation not in operations + or not isinstance(operands, list) + or not operands + ): + continue + if operation in {"subtract", "divide"} and len(operands) != 2: + continue + try: + operand_values = [ + values[ref] if isinstance(ref, str) and ref in values else parse_decimal(ref) + for ref in operands + ] + if operation == "add": + computed = sum(operand_values, Decimal("0")) + elif operation == "subtract": + computed = operand_values[0] - operand_values[1] + elif operation == "multiply": + computed = Decimal("1") + for value in operand_values: + computed *= value + else: + if operand_values[1] == 0: + continue + computed = operand_values[0] / operand_values[1] + reported_decimal = parse_decimal(reported) + except (InvalidOperation, ValueError, TypeError, ZeroDivisionError): + remaining.append(item) + continue + if not reported_decimal.is_finite() or reported_decimal != computed: + continue + key = name.strip() + values[key] = computed + unit = item.get("unit") or "" + rendered.append( + f"- derived {key} = {computed}{unit} " + f"({operation}: {', '.join(str(ref) for ref in operands)})" + ) + progressed = True + if not progressed: + break + pending = remaining + + return "\n".join(rendered) def _detect_anchors(question: str) -> List[str]: @@ -46,6 +227,19 @@ def _detect_anchors(question: str) -> List[str]: return anchors if len(anchors) >= 2 else [] +def _needs_claim_check(question: str, options: Sequence[str]) -> bool: + """Enable relation checking only for claims that actually express one.""" + text = " ".join([question, *(str(option) for option in options)]) + return bool(_CLAIM_RELATION.search(text)) + + +def _needs_calculation_judgement(question: str, options: Sequence[str]) -> bool: + """Use a second Qwen pass only when the question explicitly requires math.""" + # Do not trigger merely because a distractor contains a percentage, amount, + # or per-share figure. The stem must itself ask for a computation/order. + return bool(_CALCULATION_MARKER.search(str(question))) + + _SYSTEM_PROMPT = ( "You are a precise financial document analyst. Answer the multiple-choice " "question using ONLY the provided context. Reply with the option letter(s) " @@ -54,16 +248,90 @@ def _detect_anchors(question: str) -> List[str]: "every correct letter with no separators (e.g. ACD). If the context is " "insufficient, output the single best-supported letter." ) +_STRUCTURED_SYSTEM_PROMPT = ( + "You are a precise financial document analyst. Use only the supplied context. " + "Follow the requested compact option-state format exactly and do not add explanations." +) + + +def _parse_structured_answer(raw: str, qtype: str, num_options: int) -> str: + """Parse one-call option states, with a safe fallback for nonconforming output.""" + text = raw or "" + state_pairs = [] + for line in text.splitlines(): + match = _OPTION_RELATION_STATE.search(line) or _OPTION_STATE.search(line) + if match: + state_pairs.append((match.group(1), match.group(2))) + states = { + label.upper(): state.upper() + for label, state in state_pairs + if ord("A") <= ord(label.upper()) < ord("A") + max(1, num_options) + } + if states: + supported = "".join( + label for label in sorted(states) if states[label] == "SUPPORTED" + ) + if qtype == "multi": + return supported + if supported: + return supported[0] + answer_line = _ANSWER_LINE.search(text) + if answer_line: + return parse_answer(answer_line.group(1), qtype, num_options=num_options) + return parse_answer(text, qtype, num_options=num_options) + + +def _is_canonical_answer(answer: str, qtype: str, num_options: int) -> bool: + allowed = {chr(ord("A") + i) for i in range(max(1, num_options))} + if qtype in {"mcq", "tf"}: + return answer in allowed and (qtype != "tf" or answer in {"A", "B"}) + return bool(answer) and set(answer) <= allowed and answer == "".join(sorted(set(answer))) + + +def _merge_evidence_blocks(contexts: Sequence[str], max_chars: int) -> str: + """Deduplicate source blocks and enforce one final context budget.""" + if max_chars <= 0: + return "" + blocks: List[str] = [] + seen = set() + used = 0 + for context in contexts: + for block in (part.strip() for part in (context or "").split("\n\n")): + if not block: + continue + key = re.sub(r"\s+", " ", block).strip().lower() + if key in seen: + continue + separator = 2 if blocks else 0 + remaining = max_chars - used - separator + if remaining <= 0: + return "\n\n".join(blocks) + seen.add(key) + selected = block[:remaining] + blocks.append(selected) + used += separator + len(selected) + if len(selected) < len(block): + return "\n\n".join(blocks) + return "\n\n".join(blocks) @dataclass class RetrievalConfig: + # ``hybrid`` preserves the original research path. ``lexical`` is the + # dependency-light path used for local/offline testing; it needs no encoder + # download and no GPU. + retrieval_backend: str = "hybrid" # "hybrid" | "lexical" + competition_mode: bool = False # opt-in future API validation only + token_budget: int = 5_000_000 + usage_ledger_path: Optional[str] = None + formal_run_id: Optional[str] = None + formal_ledger_path: Optional[str] = None # max_chars stays at or below the encoder's token window (e5: 512) so a whole # chunk is embedded rather than silently truncated. max_chars: int = 450 overlap_chars: int = 80 top_k: int = 6 - compression_budget: int = 1200 + compression_budget: int = 3000 max_answer_tokens: int = 16 # -- hybrid retrieval (dense cosine + lexical BM25, fused with RRF) -- hybrid: bool = True # fuse BM25 with cosine when FTS5 is available @@ -73,8 +341,28 @@ class RetrievalConfig: w_sparse: float = 0.3 # lexical (BM25) fusion weight center: bool = True # anisotropy-robust centered cosine for the dense channel # -- multi-query retrieval (per-option sub-queries, union) -- - multi_query: bool = False # retrieve the question + each option separately, then union - multi_query_cap: int = 8 # max chunks kept after the union + multi_query: bool = True # retrieve the question + each option separately, then union + multi_query_cap: int = 12 # max chunks kept after the union + option_audit: bool = False # opt-in; audit can add cost without improving every task + option_audit_max_tokens: int = 256 + multi_option_audit: bool = True # independently check every option on multi-choice + structured_judgement: bool = True + structured_judgement_max_tokens: int = 256 + calculation_judgement: bool = True + calculation_judgement_max_tokens: int = 512 + option_evidence_budget: int = 900 + option_evidence_per_document: bool = True + # Deterministic, option-local gap filling. It is deliberately enabled by + # default: it changes only local retrieval, never the Qwen answer policy. + evidence_coverage_supplement: bool = True + evidence_coverage_max_anchors: int = 4 + evidence_coverage_supplement_budget: int = 900 + structure_index: bool = True + structure_neighbor_expansion: bool = True + structure_neighbor_cap: int = 2 + answer_context_budget: int = 12_000 + adaptive_multi_review: bool = False + adaptive_multi_review_max_tokens: int = 128 class MemoryQAAgent: @@ -82,20 +370,72 @@ class MemoryQAAgent: def __init__( self, - encoder: Optional[SentenceEncoder] = None, - store: Optional[PersistentKnowledgeMemory] = None, + encoder: Optional[Any] = None, + store: Optional[Any] = None, chat_client=None, compressor: Optional[ExtractiveCompressor] = None, config: Optional[RetrievalConfig] = None, db_path: str = ":memory:", ): - self.encoder = encoder or SentenceEncoder() self.config = config or RetrievalConfig() - # The store's key_dim must equal the encoder's output dim; probe once. - self.store = store or PersistentKnowledgeMemory( - key_dim=self.encoder.dim, db_path=db_path + if self.config.retrieval_backend not in {"hybrid", "lexical"}: + raise ValueError( + "retrieval_backend must be 'hybrid' or 'lexical', got " + f"{self.config.retrieval_backend!r}" + ) + + if self.config.retrieval_backend == "lexical": + if encoder is not None: + raise ValueError("lexical retrieval must not receive an encoder") + from ..memory.lexical_store import LexicalKnowledgeMemory + + self.encoder = None + self.store = store or LexicalKnowledgeMemory(db_path=db_path) + if not hasattr(self.store, "search_bm25"): + raise TypeError("lexical retrieval requires a lexical store") + else: + # Keep the vector/embedding path lazy so importing or using the + # lexical path never initializes a model or downloads weights. + from ..memory.encoder import SentenceEncoder + from ..memory.knowledge_store import PersistentKnowledgeMemory + + self.encoder = encoder or SentenceEncoder() + # The store's key_dim must equal the encoder's output dim; probe once. + self.store = store or PersistentKnowledgeMemory( + key_dim=self.encoder.dim, db_path=db_path + ) + + self.chat_client = chat_client or build_chat_client( + competition_mode=self.config.competition_mode, + token_budget=self.config.token_budget, + usage_ledger_path=self.config.usage_ledger_path, + formal_run_id=self.config.formal_run_id, + formal_ledger_path=self.config.formal_ledger_path, ) - self.chat_client = chat_client or build_chat_client() + if self.config.competition_mode: + # Formal execution has exactly one accepted chat dependency. Exact + # type checking prevents an injected look-alike/subclass from + # bypassing Qwen's provider, ledger, or transport-denial gates. + if type(self.chat_client) is not QwenChatClient: + raise RuntimeError( + "formal mode requires a validated formal QwenChatClient; " + "mock or arbitrary injected clients are forbidden" + ) + if self.config.token_budget != DEFAULT_TOKEN_BUDGET: + raise ValueError( + f"formal mode requires token_budget={DEFAULT_TOKEN_BUDGET}" + ) + self.chat_client.assert_formal_configuration() + if not isinstance(self.config.formal_run_id, str) or not self.config.formal_run_id.strip(): + raise ValueError("formal mode requires a non-empty formal_run_id") + if not isinstance(self.config.formal_ledger_path, str) or not self.config.formal_ledger_path.strip(): + raise ValueError("formal mode requires an explicit formal_ledger_path") + if self.chat_client.formal_run_id != self.config.formal_run_id.strip(): + raise ValueError("formal Qwen client run id does not match agent configuration") + if self.chat_client.formal_ledger_path != str( + Path(self.config.formal_ledger_path).expanduser().resolve(strict=False) + ): + raise ValueError("formal Qwen client ledger path does not match agent configuration") self.compressor = compressor or ExtractiveCompressor( budget_chars=self.config.compression_budget ) @@ -103,18 +443,25 @@ def __init__( # -- ingest ----------------------------------------------------------- def ingest_document(self, doc_id: str, text: str) -> int: - """Chunk, embed and store one document. Returns the number of chunks.""" - chunks = chunk_document( + """Ingest exact text with deterministic structure metadata.""" + nodes = build_evidence_nodes( doc_id, text, max_chars=self.config.max_chars, overlap_chars=self.config.overlap_chars, ) - for ch in chunks: - key = self.encoder.encode(ch.text, is_query=False) - self.store.write(key, ch.text, meta=ch.as_meta()) + for node in nodes: + meta = node.as_meta() if self.config.structure_index else { + "doc_id": node.doc_id, + "chunk_idx": node.chunk_idx, + } + if self.config.retrieval_backend == "lexical": + self.store.write(node.content, meta=meta) + else: + key = self.encoder.encode(node.content, is_query=False) + self.store.write(key, node.content, meta=meta) self._ingested_docs.add(str(doc_id)) - return len(chunks) + return len(nodes) def ingest_documents(self, docs: Dict[str, str]) -> int: """Ingest a ``{doc_id: text}`` mapping. Returns total chunks stored.""" @@ -135,7 +482,7 @@ def _enrich(question: str, options: Optional[Sequence[str]]) -> str: def _hybrid_hits( self, query_text: str, allowed: Optional[List[str]], limit: int - ) -> List[Tuple[int, str]]: + ) -> List[Tuple[int, str, Any]]: """One retrieval pass for *query_text*: dense (cosine) and lexical (BM25) channels fused with RRF, returning ``(id, content)`` best-first. Falls back to dense-only when hybrid is off or FTS5 is unavailable.""" @@ -147,19 +494,87 @@ def _hybrid_hits( ) dense_hits = [(h[0], h[1], h[3]) for h in dense] # (id, content, meta) if not use_hybrid: - return [(rid, c) for rid, c, _m in dense_hits[:limit]] + return dense_hits[:limit] sparse_hits = self.store.search_bm25(query_text, top_k=pool, doc_ids=allowed) fused = rrf_fuse( dense_hits, sparse_hits, k=self.config.rrf_k, w_dense=self.config.w_dense, w_sparse=self.config.w_sparse, top_k=limit, ) - return [(rid, c) for rid, c, _m in fused] + return fused + + def _lexical_hits( + self, query_text: str, allowed: Optional[List[str]], limit: int + ) -> List[Tuple[int, str, Any]]: + """Retrieve only through local FTS5/BM25, with no dense fallback.""" + if not getattr(self.store, "fts_enabled", False): + if self.config.competition_mode: + raise RuntimeError("lexical retrieval requires SQLite FTS5") + return [] + hits = self.store.search_bm25(query_text, top_k=limit, doc_ids=allowed) + return hits + + @staticmethod + def _format_passage(content: str, meta: Any) -> str: + """Keep source provenance attached to every retrieved passage.""" + if isinstance(meta, dict): + source = meta.get("doc_id", "unknown") + chunk = meta.get("chunk_idx") + suffix = f" chunk={chunk}" if chunk is not None else "" + page = meta.get("page") + if page is not None: + suffix += f" page={page}" + section = meta.get("section") + if section: + suffix += f" section={section}" + clause = meta.get("clause_id") + if clause: + suffix += f" clause={clause}" + else: + source = "unknown" + suffix = "" + return f"[source doc_id={source}{suffix}]\n{content}" + + def _expand_structural_hits(self, hits): + if ( + self.config.retrieval_backend != "lexical" + or not self.config.structure_index + or not self.config.structure_neighbor_expansion + ): + return hits + return self.store.expand_neighbors( + hits, max_extra=self.config.structure_neighbor_cap + ) + + def _locate_documents( + self, query_text: str, options: Optional[Sequence[str]] + ) -> List[str]: + """Find B榜 candidate documents before searching their passages. + + This is deliberately a small lexical locator. It prevents an omitted + ``doc_ids`` value from silently turning a formal run into an unrestricted + dense search while keeping the local test path simple. + """ + if not getattr(self.store, "fts_enabled", False): + raise RuntimeError("document locator requires SQLite FTS5") + query = self._enrich(query_text, options) + hits = self.store.search_bm25(query, top_k=max(32, self.config.top_k * 4)) + candidates: List[str] = [] + seen = set() + for _rid, _content, meta in hits: + if not isinstance(meta, dict) or meta.get("doc_id") is None: + continue + doc_id = str(meta["doc_id"]) + if doc_id not in seen: + seen.add(doc_id) + candidates.append(doc_id) + return candidates def retrieve( self, question: str, doc_ids: Optional[Sequence[str]] = None, options: Optional[Sequence[str]] = None, + split: Optional[str] = None, ) -> List[str]: """Return the top chunk texts for *question*, restricted to *doc_ids*. @@ -177,9 +592,42 @@ def retrieve( operand. Retrieval is local, so this costs no generation tokens; the extra chunks are still bounded by ``multi_query_cap`` and the compression budget. """ + normalized_split = split.upper() if isinstance(split, str) else None + if normalized_split not in {None, "A", "B"}: + raise ValueError("split must be A or B") + if normalized_split == "A" and not doc_ids: + raise ValueError("split A requires non-empty doc_ids") + if normalized_split == "B" and doc_ids: + raise ValueError("split B must not receive doc_ids") + + allowed: Optional[List[str]] + if normalized_split == "B": + allowed = self._locate_documents(question, options) + if not allowed: + raise RuntimeError("B candidate locator found no documents") + elif doc_ids is None: + allowed = None + else: + allowed = [str(d) for d in doc_ids] + if not allowed: + if self.config.competition_mode: + raise ValueError("an explicit document filter cannot be empty") + return [] + + if self.config.competition_mode: + if self.config.retrieval_backend != "lexical": + raise RuntimeError( + "competition mode is opt-in and requires retrieval_backend='lexical'" + ) + if allowed is None: + allowed = self._locate_documents(question, options) + if not allowed: + raise RuntimeError("document locator found no candidate documents") + elif not self.store.has_doc_ids(allowed): + raise ValueError(f"unknown document id in filter: {allowed}") + if len(self.store) == 0: return [] - allowed = [str(d) for d in doc_ids] if doc_ids else None if self.config.multi_query and (options or _detect_anchors(question)): # Sub-queries: the bare question, one anchor-boosted query per temporal @@ -187,22 +635,69 @@ def retrieve( # sides), and one option-boosted query per option (for fact-matching # mcq, where the answer sentence echoes an option's wording). subqueries = [question] - subqueries += [f"{a} {question}" for a in _detect_anchors(question)] + # Keep the temporal channel narrowly anchored. Adding the full + # question here turns a precise year/quarter lookup into a large + # OR query whose tied BM25 results can crowd out the very operand + # this channel exists to protect. The question and option + # channels below still provide semantic/lexical context. + anchor_queries = list(_detect_anchors(question)) + subqueries += anchor_queries subqueries += [f"{question} {o}" for o in (options or [])] - best: Dict[int, Tuple[float, str]] = {} + best: Dict[int, Tuple[float, str, Any]] = {} + required_anchor_ids: List[int] = [] for sq in subqueries: - for rank, (rid, content) in enumerate( - self._hybrid_hits(sq, allowed, self.config.top_k) - ): + hits = ( + self._lexical_hits(sq, allowed, self.config.top_k) + if self.config.retrieval_backend == "lexical" + else self._hybrid_hits(sq, allowed, self.config.top_k) + ) + if sq in anchor_queries and hits: + # Keep one best passage for every temporal operand before + # global fusion fills the remaining budget. + required_anchor_ids.append(hits[0][0]) + for rank, (rid, content, meta) in enumerate(hits): score = 1.0 / (1 + rank) # best rank of this chunk across sub-queries if rid not in best or score > best[rid][0]: - best[rid] = (score, content) - ranked = sorted(best.values(), key=lambda sc: sc[0], reverse=True) + best[rid] = (score, content, meta) + ranked = sorted(best.items(), key=lambda item: item[1][0], reverse=True) cap = max(self.config.top_k, self.config.multi_query_cap) - return [content for _score, content in ranked[:cap]] + required_ids = list(dict.fromkeys(required_anchor_ids)) + # A question may explicitly reference several documents. Global + # BM25 ranking can otherwise spend the entire cap on the document + # whose wording happens to overlap most, hiding evidence from the + # other referenced documents. Reserve the best retrieved chunk + # from every allowed document before filling the remaining budget. + if allowed and len(allowed) > 1: + best_by_doc: Dict[str, int] = {} + for rid, (_score, _content, meta) in ranked: + if not isinstance(meta, dict): + continue + doc_id = str(meta.get("doc_id", "")) + if doc_id and doc_id in allowed and doc_id not in best_by_doc: + best_by_doc[doc_id] = rid + required_ids.extend(best_by_doc.values()) + selected_ids = required_ids + [ + rid for rid, _value in ranked if rid not in set(required_ids) + ] + selected = [best[rid] for rid in selected_ids[:cap]] + selected_hits = [ + (rid, content, meta) + for rid, (_score, content, meta) in zip(selected_ids, selected) + ] + selected_hits = self._expand_structural_hits(selected_hits) + return [ + self._format_passage(content, meta) + for _rid, content, meta in selected_hits + ] enriched = self._enrich(question, options) - return [c for _rid, c in self._hybrid_hits(enriched, allowed, self.config.top_k)] + hits = ( + self._lexical_hits(enriched, allowed, self.config.top_k) + if self.config.retrieval_backend == "lexical" + else self._hybrid_hits(enriched, allowed, self.config.top_k) + ) + hits = self._expand_structural_hits(hits) + return [self._format_passage(content, meta) for _rid, content, meta in hits] # -- answer ----------------------------------------------------------- def answer_question( @@ -212,27 +707,264 @@ def answer_question( options: Sequence[str], qtype: str = "mcq", doc_ids: Optional[Sequence[str]] = None, + split: Optional[str] = None, ) -> AnswerResult: """Answer one question and report the tokens it cost.""" - passages = self.retrieve(question, doc_ids=doc_ids, options=options) + passages = self.retrieve( + question, doc_ids=doc_ids, options=options, split=split + ) # Compress against question + options so an answer sentence matching a # distractor's wording is retained, not dropped. - compressed = self.compressor.compress(self._enrich(question, options), passages) - user_prompt = self._build_prompt(question, options, qtype, compressed.text) + compressed = self.compressor.compress( + self._enrich(question, options), + passages, + coverage_queries=[question, *options], + ) + context = compressed.text + option_evidence = [] + evidence_scope = None + if self.config.option_evidence_per_document: + if isinstance(split, str) and split.upper() == "B": + evidence_scope = self._locate_documents(question, options) + elif doc_ids: + evidence_scope = [str(doc_id) for doc_id in doc_ids] + for label, option in zip( + (chr(ord("A") + i) for i in range(len(options))), options + ): + if evidence_scope: + option_passages = [] + for evidence_doc_id in evidence_scope: + option_passages.extend( + self.retrieve( + question, + doc_ids=[evidence_doc_id], + options=[option], + split="A", + ) + ) + else: + option_passages = self.retrieve( + question, + doc_ids=doc_ids, + options=[option], + split=split, + ) + option_compressed = ExtractiveCompressor( + budget_chars=self.config.option_evidence_budget + ).compress( + option, + option_passages, + coverage_queries=[question, option], + ) + option_text = option_compressed.text + if self.config.evidence_coverage_supplement: + missing_targets = [ + *_missing_evidence_anchors(question, option, option_text), + *_missing_evidence_slots(question, option, option_text), + ][: self.config.evidence_coverage_max_anchors] + supplement_texts = [] + for target in missing_targets: + # Each supplement is a narrow lexical query. It does not + # alter the primary ranking and never invokes Qwen. + supplement_passages = self.retrieve( + target, + doc_ids=evidence_scope if evidence_scope else doc_ids, + split="A" if evidence_scope else split, + ) + supplement = ExtractiveCompressor( + budget_chars=self.config.evidence_coverage_supplement_budget + ).compress( + target, + supplement_passages, + coverage_queries=[target], + ) + if supplement.text: + supplement_texts.append(supplement.text) + if supplement_texts: + option_text = "\n".join([option_text, *supplement_texts]) + option_evidence.append( + f"Option {label} evidence:\n" + + (option_text or "(no option-specific evidence)") + ) + context = _merge_evidence_blocks( + [context, *option_evidence], self.config.answer_context_budget + ) + audit_text = "" + audit_usage = TokenUsage() + if self.config.option_audit or ( + qtype == "multi" and self.config.multi_option_audit + ): + audit_result: ChatResult = self.chat_client.chat( + messages=[ + { + "role": "system", + "content": ( + "You are an evidence auditor. Use ONLY the provided context. " + "Evaluate every option independently. For each option output " + "one compact line: LETTER=SUPPORTED, REFUTED, or INSUFFICIENT; " + "then cite the shortest supporting or contradicting phrase. " + "Do not give a final answer set." + ), + }, + { + "role": "user", + "content": self._build_audit_prompt( + question, options, qtype, context + ), + }, + ], + temperature=0.0, + max_tokens=self.config.option_audit_max_tokens, + ) + audit_text = audit_result.text.strip() + audit_usage = audit_result.usage + calculation_draft = "" + calculation_usage = TokenUsage() + if ( + self.config.structured_judgement + and not audit_text + and self.config.calculation_judgement + and _needs_calculation_judgement(question, options) + ): + calculation_result: ChatResult = self.chat_client.chat( + messages=[ + {"role": "system", "content": _STRUCTURED_SYSTEM_PROMPT}, + { + "role": "user", + "content": self._build_calculation_prompt( + question, options, context + ), + }, + ], + temperature=0.0, + max_tokens=self.config.calculation_judgement_max_tokens, + ) + calculation_draft = _verify_calculation_draft(calculation_result.text) + calculation_usage = calculation_result.usage + user_prompt = ( + self._build_structured_prompt( + question, options, qtype, context, calculation_draft=calculation_draft + ) + if self.config.structured_judgement and not audit_text + else self._build_prompt( + question, options, qtype, context, audit_text=audit_text + ) + ) result: ChatResult = self.chat_client.chat( messages=[ - {"role": "system", "content": _SYSTEM_PROMPT}, + { + "role": "system", + "content": ( + _STRUCTURED_SYSTEM_PROMPT + if self.config.structured_judgement + else _SYSTEM_PROMPT + ), + }, {"role": "user", "content": user_prompt}, ], temperature=0.0, - max_tokens=self.config.max_answer_tokens, + max_tokens=( + self.config.structured_judgement_max_tokens + if self.config.structured_judgement + else self.config.max_answer_tokens + ), + ) + answer = ( + _parse_structured_answer(result.text, qtype, len(options) or 4) + if self.config.structured_judgement + else parse_answer(result.text, qtype, num_options=len(options) or 4) + ) + # A provider can occasionally return an empty or non-canonical boolean + # despite the structured prompt. Retry the format conversion once + # without changing the retrieved evidence or the underlying question. + # Never guess a side of a true/false question locally. + if not _is_canonical_answer(answer, qtype, len(options) or 4): + retry_usage = TokenUsage() + retry_options = "\n".join( + f"{chr(ord('A') + i)}: {option}" for i, option in enumerate(options) + ) + retry_prompt = ( + f"Question: {question}\nOptions:\n{retry_options}\n" + f"Evidence:\n{context[:12000]}\n" + ) + for _ in range(3): + if qtype == "tf": + instruction = "return A if the statement is true/correct, otherwise return B" + elif qtype == "multi": + instruction = "return every correct option letter, sorted with no separators" + else: + instruction = "return exactly one valid option letter" + retry = self.chat_client.chat( + messages=[ + { + "role": "system", + "content": ( + "Return only the canonical answer. No other text. " + + instruction + "." + ), + }, + { + "role": "user", + "content": ( + "Using the same question and evidence above, " + + instruction + ".\n\n" + + retry_prompt + ), + }, + ], + temperature=0.0, + max_tokens=8, + ) + retry_usage = retry_usage + retry.usage + retry_answer = parse_answer(retry.text, qtype, num_options=len(options) or 4) + if _is_canonical_answer(retry_answer, qtype, len(options) or 4): + answer = retry_answer + break + if _is_canonical_answer(answer, qtype, len(options) or 4): + result = ChatResult( + text=result.text, + usage=result.usage + retry_usage, + ) + review_usage = TokenUsage() + if ( + self.config.adaptive_multi_review + and qtype == "multi" + and len(answer) <= 1 + ): + review_result = self.chat_client.chat( + messages=[ + { + "role": "system", + "content": _STRUCTURED_SYSTEM_PROMPT, + }, + { + "role": "user", + "content": self._build_multi_review_prompt( + question, options, context, answer + ), + }, + ], + temperature=0.0, + max_tokens=self.config.adaptive_multi_review_max_tokens, + ) + review_usage = review_result.usage + reviewed = _parse_structured_answer( + review_result.text, qtype, len(options) or 4 + ) + if _is_canonical_answer(reviewed, qtype, len(options) or 4): + answer = reviewed + return AnswerResult( + qid=str(qid), answer=answer, qtype=qtype, + usage=audit_usage + calculation_usage + result.usage + review_usage, ) - answer = parse_answer(result.text, qtype, num_options=len(options) or 4) - return AnswerResult(qid=str(qid), answer=answer, qtype=qtype, usage=result.usage) @staticmethod def _build_prompt( - question: str, options: Sequence[str], qtype: str, context: str + question: str, + options: Sequence[str], + qtype: str, + context: str, + audit_text: str = "", ) -> str: labels = [chr(ord("A") + i) for i in range(len(options))] opt_lines = "\n".join(f"{lab}) {opt}" for lab, opt in zip(labels, options)) @@ -242,9 +974,144 @@ def _build_prompt( "multi": "Multiple choice: reply with all correct letters, e.g. ABD.", }.get(qtype, "Reply with the option letter(s).") ctx = context or "(no relevant context retrieved)" + claim_check = ( + "Before choosing, silently check each option as a claim: identify its subject, " + "metric, time period, qualifiers, and stated relation. For greater/less than, " + "compare the explicitly stated quantities only when their units and periods " + "match; do not reverse the relation or substitute a nearby but different " + "quantity. Treat absent evidence as insufficient rather than as a contradiction.\n" + if _needs_claim_check(question, options) + else "" + ) return ( f"Context:\n{ctx}\n\n" + + ( + "Option evidence audit (a draft; verify it against Context):\n" + + audit_text + + "\n\n" + if audit_text + else "" + ) + + f"Question: {question}\n" + + f"Options:\n{opt_lines}\n\n" + + f"{kind}\n" + + claim_check + + ("Evaluate every option independently against the context; include a letter only when that option is supported.\n" + if qtype == "multi" else "") + + "Answer:" + ) + + @staticmethod + def _build_structured_prompt( + question: str, + options: Sequence[str], + qtype: str, + context: str, + calculation_draft: str = "", + ) -> str: + labels = [chr(ord("A") + i) for i in range(len(options))] + opt_lines = "\n".join( + f"{label}) {option}" for label, option in zip(labels, options) + ) + relation = ( + "For every comparison claim, first identify the two compared quantities and " + "write each option exactly as LETTER: left=value; relation=; " + "right=value; state=SUPPORTED|REFUTED|INSUFFICIENT before assigning " + "the state. Preserve the question's direction and compare only matching units " + "and periods; do not substitute a nearby unrelated number. A claim is supported " + "when it is directly entailed by the context, even if the context contains " + "additional detail beyond the wording of the option. For an ordering/ranking " + "claim, list every object and its computed value, and support the option only " + "when the complete ordering and all stated values match.\n" + if _needs_claim_check(question, options) + else ( + "A multi-choice option is supported when its complete claim is directly " + "entailed by the context, even if the context contains additional detail. " + "For multi-choice questions, evaluate each option independently and " + "combine evidence across passages and referenced documents; do not require " + "one passage to support every option, and do not infer support from silence " + "or a related product.\n" + if qtype == "multi" + else "" + ) + ) + return ( + f"Context:\n{context or '(no relevant context retrieved)'}\n\n" + + ( + "A Qwen calculation draft is provided below. Verify it against Context " + "and Question; correct it if unsupported. Do not use unsupported values.\n" + "CALCULATION DRAFT:\n" + + calculation_draft + + "\n\n" + if calculation_draft + else "" + ) + + f"Question: {question}\nOptions:\n{opt_lines}\n\n" + "Evaluate every option independently using only direct evidence in Context. " + "Do not infer a positive claim from silence or from a related product. " + + relation + + "Output exactly one line per option in the form LETTER=STATE, where STATE is " + "SUPPORTED, REFUTED, or INSUFFICIENT, followed by one final line ANSWER:LETTERS. " + "Include only SUPPORTED letters in ANSWER, sorted; exclude INSUFFICIENT. " + "Do not explain." + ) + + @staticmethod + def _build_calculation_prompt( + question: str, options: Sequence[str], context: str + ) -> str: + labels = [chr(ord("A") + i) for i in range(len(options))] + opt_lines = "\n".join( + f"{label}) {option}" for label, option in zip(labels, options) + ) + return ( + f"Context:\n{context or '(no relevant context retrieved)'}\n\n" + f"Question: {question}\nOptions:\n{opt_lines}\n\n" + "Do not choose an option. Return JSON only with this shape: " + '{"facts":[{"name":"fact name","value":"12.5","unit":"万元"}],' + '"derived":[{"name":"result name","operation":"add",' + '"operands":["fact name"],"value":"12.5","unit":"万元"}]}.' + "Extract only numerical inputs explicitly stated in the question and " + "formulas or rates explicitly stated in Context. Use only add, subtract, " + "multiply, or divide. Do not use numbers from answer options as inputs, " + "do not invent fees or rates, and do not include a derived value unless " + "its operands are present in facts." + ) + + @staticmethod + def _build_audit_prompt( + question: str, options: Sequence[str], qtype: str, context: str + ) -> str: + labels = [chr(ord("A") + i) for i in range(len(options))] + opt_lines = "\n".join(f"{lab}) {opt}" for lab, opt in zip(labels, options)) + return ( + f"Context:\n{context or '(no relevant context retrieved)'}\n\n" f"Question: {question}\n" f"Options:\n{opt_lines}\n\n" - f"{kind}\nAnswer:" + f"Answer format: {qtype}.\n" + "Audit each option independently." + ) + + @staticmethod + def _build_multi_review_prompt( + question: str, + options: Sequence[str], + context: str, + initial_answer: str, + ) -> str: + labels = [chr(ord("A") + i) for i in range(len(options))] + opt_lines = "\n".join( + f"{label}) {option}" for label, option in zip(labels, options) + ) + return ( + f"Initial answer: {initial_answer or '(empty)'}\n" + f"Question: {question}\nOptions:\n{opt_lines}\n\n" + f"Context:\n{context or '(no relevant context retrieved)'}\n\n" + "The initial answer may have omitted correct options. Re-evaluate every " + "option independently against the context, especially options omitted from " + "the initial answer. A multi-choice option is SUPPORTED only when its complete " + "claim is directly entailed; do not treat silence as REFUTED. Output exactly " + "one line per option as LETTER=SUPPORTED, LETTER=REFUTED, or " + "LETTER=INSUFFICIENT, followed by ANSWER:LETTERS containing all and only " + "SUPPORTED options. Do not explain." ) diff --git a/awareliquid/adapter/qwen_client.py b/awareliquid/adapter/qwen_client.py index 89cbf53..8dc6d48 100644 --- a/awareliquid/adapter/qwen_client.py +++ b/awareliquid/adapter/qwen_client.py @@ -12,9 +12,11 @@ AWARELIQUID_LLM_BASE_URL OpenAI-compatible base URL (default DashScope compatible-mode endpoint) AWARELIQUID_LLM_MODEL model id (default "qwen-plus") + AWARELIQUID_ALLOW_FORMAL_NETWORK=1 explicitly enables formal Qwen egress; + the default is deny. -When no API key is present the client transparently falls back to a deterministic -``MockChatClient`` so demos, unit tests and offline development keep working. +Mock is available only by explicit test-only selection. Formal execution never +falls back to a mock client or a different transport. """ from __future__ import annotations @@ -22,15 +24,38 @@ import json import os import re +import tempfile +import threading import urllib.error import urllib.request +from contextlib import contextmanager from dataclasses import dataclass, field +from pathlib import Path from typing import Dict, List, Optional +from urllib.parse import urlparse + +import fcntl DEFAULT_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1" DEFAULT_MODEL = "qwen-plus" - - +DEFAULT_PROVIDER = "qwen" +DEFAULT_TOKEN_BUDGET = 5_000_000 + +# Formal runs must agree on one durable aggregate. This registry deliberately +# lives in this transport module, next to the sole formal client, so a second +# client cannot silently point the same run at an alternate ledger. +_FORMAL_LEDGER_PATHS: Dict[str, str] = {} +_FORMAL_LEDGER_PATHS_LOCK = threading.RLock() + +# Formal transport is deny-by-default. A real execution environment must opt in +# explicitly; all provider, model, endpoint, and token-ledger checks still run. +_FORMAL_NETWORK_ENV = "AWARELIQUID_ALLOW_FORMAL_NETWORK" +_ALLOWED_QWEN_MODELS = { + "qwen-plus", "qwen-turbo", "qwen-max", "qwen-long", + "qwen3.6-plus", + "qwen3-235b-a22b", "qwen3-32b", "qwen3-30b-a3b", "qwen3-14b", + "qwen3-8b", "qwen3-4b", "qwen3-1.7b", "qwen3-0.6b", +} @dataclass class TokenUsage: """Token counts for a single call (mirrors the API ``usage`` object).""" @@ -54,6 +79,199 @@ def as_dict(self) -> Dict[str, int]: } +class UsageLedger: + """Fail-closed, process-safe global token-budget ledger. + + ``path`` is optional. When supplied, committed usage is persisted after + every reconciliation so a crashed submission cannot silently lose its + accounting record. Each operation refreshes committed usage while holding a + per-ledger file lock; reconciliation then atomically replaces the ledger + file before releasing that lock. This prevents independent clients from + overwriting each other's observed usage. Reservations remain in-memory and + are released when a request fails before a usage object is received. + """ + + def __init__(self, max_tokens: int = DEFAULT_TOKEN_BUDGET, path: Optional[str] = None): + if isinstance(max_tokens, bool) or not isinstance(max_tokens, int) or max_tokens <= 0: + raise ValueError("max_tokens must be a positive integer") + self.max_tokens = max_tokens + self.path = Path(path) if path else None + self.used_tokens = 0 + self._reserved_tokens = 0 + self._lock = threading.RLock() + self._persistence_error: Optional[RuntimeError] = None + self._load() + + @property + def is_persistent(self) -> bool: + return self.path is not None + + def reserve(self, estimated_tokens: int) -> int: + """Reserve an upper bound before an API call and return that amount.""" + if isinstance(estimated_tokens, bool) or not isinstance(estimated_tokens, int): + raise ValueError("estimated_tokens must be an integer") + if estimated_tokens < 0: + raise ValueError("estimated_tokens cannot be negative") + amount = estimated_tokens + with self._lock: + self._raise_if_persistence_failed() + with self._persistent_lock(): + self._refresh_persisted_usage_locked() + if self.used_tokens + self._reserved_tokens + amount > self.max_tokens: + raise RuntimeError( + "Qwen token budget exhausted: " + f"used={self.used_tokens}, reserved={self._reserved_tokens}, " + f"requested={amount}, limit={self.max_tokens}" + ) + self._reserved_tokens += amount + return amount + + def release(self, reserved_tokens: int) -> None: + with self._lock: + if ( + isinstance(reserved_tokens, bool) + or not isinstance(reserved_tokens, int) + or reserved_tokens < 0 + or reserved_tokens > self._reserved_tokens + ): + raise RuntimeError("invalid Qwen token reservation release") + self._reserved_tokens -= reserved_tokens + + def reconcile(self, reserved_tokens: int, usage: TokenUsage) -> None: + with self._lock: + self._raise_if_persistence_failed() + if not isinstance(usage, TokenUsage): + raise TypeError("usage must be TokenUsage") + if ( + isinstance(reserved_tokens, bool) + or not isinstance(reserved_tokens, int) + or reserved_tokens < 0 + or reserved_tokens > self._reserved_tokens + ): + raise RuntimeError("invalid Qwen token reservation reconciliation") + counts = usage.as_dict() + if any(isinstance(value, bool) or not isinstance(value, int) or value < 0 for value in counts.values()): + raise ValueError("usage fields must be non-negative integers") + if usage.total_tokens != usage.prompt_tokens + usage.completion_tokens: + raise ValueError("usage.total_tokens must equal prompt_tokens + completion_tokens") + actual = usage.total_tokens + with self._persistent_lock(): + # A different client or process may have reconciled since this + # instance reserved its estimate. Refresh while holding the + # shared lock, then use that committed aggregate for the gate. + self._refresh_persisted_usage_locked() + if self.used_tokens + self._reserved_tokens - reserved_tokens + actual > self.max_tokens: + raise RuntimeError( + "Qwen token budget exceeded by API usage: " + f"used={self.used_tokens}, reserved={self._reserved_tokens - reserved_tokens}, " + f"actual={actual}, limit={self.max_tokens}" + ) + updated_used_tokens = self.used_tokens + actual + try: + self._persist_locked(updated_used_tokens) + except Exception as exc: + # The usage result is known but cannot be durably counted. + # Refuse future calls from this ledger rather than risk + # spending against an unverifiable aggregate. + self._persistence_error = RuntimeError( + "usage ledger persistence failed; refusing further calls" + ) + raise self._persistence_error from exc + self._reserved_tokens -= reserved_tokens + self.used_tokens = updated_used_tokens + + def snapshot(self) -> Dict[str, int]: + with self._lock: + self._raise_if_persistence_failed() + with self._persistent_lock(): + self._refresh_persisted_usage_locked() + return { + "used_tokens": self.used_tokens, + "reserved_tokens": self._reserved_tokens, + "remaining_tokens": max( + 0, self.max_tokens - self.used_tokens - self._reserved_tokens + ), + "max_tokens": self.max_tokens, + } + + def _load(self) -> None: + with self._lock: + with self._persistent_lock(): + self._refresh_persisted_usage_locked() + + def _refresh_persisted_usage_locked(self) -> None: + if self.path is None: + return + try: + data = json.loads(self.path.read_text(encoding="utf-8")) + if set(data) != {"max_tokens", "used_tokens"}: + raise ValueError("usage ledger schema is invalid") + stored_limit = data["max_tokens"] + if isinstance(stored_limit, bool) or not isinstance(stored_limit, int): + raise ValueError("usage ledger max_tokens is invalid") + if stored_limit != self.max_tokens: + raise ValueError("usage ledger max_tokens does not match current budget") + used_tokens = data["used_tokens"] + if isinstance(used_tokens, bool) or not isinstance(used_tokens, int): + raise ValueError("usage ledger used_tokens is invalid") + if used_tokens < 0 or used_tokens > self.max_tokens: + raise ValueError("usage ledger used_tokens is out of range") + self.used_tokens = used_tokens + except FileNotFoundError: + self.used_tokens = 0 + except (OSError, ValueError, TypeError, json.JSONDecodeError) as exc: + raise RuntimeError(f"invalid usage ledger: {self.path}") from exc + + @contextmanager + def _persistent_lock(self): + """Hold an advisory cross-process lock for the persistent ledger.""" + if self.path is None: + yield + return + self.path.parent.mkdir(parents=True, exist_ok=True) + lock_path = self.path.with_name(f"{self.path.name}.lock") + try: + with lock_path.open("a+b") as lock_file: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX) + try: + yield + finally: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN) + except OSError as exc: + raise RuntimeError(f"unable to lock usage ledger: {self.path}") from exc + + def _persist_locked(self, used_tokens: int) -> None: + """Atomically persist committed usage while ``_persistent_lock`` is held.""" + if self.path is None: + return + payload = {"max_tokens": self.max_tokens, "used_tokens": used_tokens} + fd, temporary_path = tempfile.mkstemp( + prefix=f".{self.path.name}.", suffix=".tmp", dir=str(self.path.parent) + ) + try: + with os.fdopen(fd, "w", encoding="utf-8") as temporary_file: + json.dump(payload, temporary_file, ensure_ascii=False, indent=2) + temporary_file.write("\n") + temporary_file.flush() + os.fsync(temporary_file.fileno()) + os.replace(temporary_path, self.path) + directory_fd = os.open(self.path.parent, os.O_RDONLY) + try: + os.fsync(directory_fd) + finally: + os.close(directory_fd) + except OSError: + try: + os.unlink(temporary_path) + except FileNotFoundError: + pass + raise + + def _raise_if_persistence_failed(self) -> None: + if self._persistence_error is not None: + raise self._persistence_error + + @dataclass class ChatResult: """Uniform return shape from every chat backend.""" @@ -75,16 +293,78 @@ def __init__( api_key: str, base_url: str = DEFAULT_BASE_URL, model: str = DEFAULT_MODEL, + provider: str = DEFAULT_PROVIDER, timeout: float = 60.0, max_retries: int = 2, + competition_mode: bool = False, + token_budget: int = DEFAULT_TOKEN_BUDGET, + usage_ledger: Optional[UsageLedger] = None, + usage_ledger_path: Optional[str] = None, + formal_run_id: Optional[str] = None, + formal_ledger_path: Optional[str] = None, ): if not api_key: raise ValueError("QwenChatClient requires a non-empty api_key") self.api_key = api_key self.base_url = base_url.rstrip("/") self.model = model + self.provider = provider self.timeout = float(timeout) self.max_retries = int(max_retries) + self.competition_mode = bool(competition_mode) + self.formal_run_id = formal_run_id + self.formal_ledger_path = ( + _canonical_ledger_path(formal_ledger_path) + if formal_ledger_path is not None + else None + ) + if usage_ledger is not None and not isinstance(usage_ledger, UsageLedger): + raise TypeError("usage_ledger must be a UsageLedger") + if self.competition_mode: + self._configure_formal_ledger( + token_budget=token_budget, + usage_ledger=usage_ledger, + usage_ledger_path=usage_ledger_path, + ) + else: + self.usage_ledger = usage_ledger if usage_ledger is not None else UsageLedger( + token_budget, usage_ledger_path + ) + if self.competition_mode: + self.assert_formal_configuration() + + def _configure_formal_ledger( + self, + *, + token_budget: int, + usage_ledger: Optional[UsageLedger], + usage_ledger_path: Optional[str], + ) -> None: + """Build the one ledger permitted for this explicitly named run.""" + if self.formal_ledger_path is None: + raise ValueError("formal mode requires an explicit formal_ledger_path") + if not isinstance(self.formal_run_id, str) or not self.formal_run_id.strip(): + raise ValueError("formal mode requires a non-empty formal_run_id") + self.formal_run_id = self.formal_run_id.strip() + if usage_ledger_path is not None: + raise ValueError( + "formal mode accepts ledger configuration only through formal_ledger_path" + ) + if usage_ledger is not None: + if ( + usage_ledger.path is None + or _canonical_ledger_path(str(usage_ledger.path)) + != self.formal_ledger_path + ): + raise ValueError( + "formal usage_ledger path must exactly match formal_ledger_path" + ) + self.usage_ledger = usage_ledger + else: + _bind_formal_ledger_path(self.formal_run_id, self.formal_ledger_path) + self.usage_ledger = UsageLedger(token_budget, self.formal_ledger_path) + if usage_ledger is not None: + _bind_formal_ledger_path(self.formal_run_id, self.formal_ledger_path) def chat( self, @@ -92,12 +372,22 @@ def chat( temperature: float = 0.0, max_tokens: int = 64, ) -> ChatResult: + if isinstance(max_tokens, bool) or not isinstance(max_tokens, int) or max_tokens <= 0: + raise ValueError("max_tokens must be a positive integer") + if self.competition_mode: + self.assert_formal_configuration() + _deny_formal_network_transport() + estimated_tokens = _estimate_tokens( + json.dumps(messages, ensure_ascii=False) + ) + int(max_tokens) + reserved_tokens = self.usage_ledger.reserve(estimated_tokens) payload = json.dumps( { "model": self.model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, + "enable_thinking": False, } ).encode("utf-8") url = f"{self.base_url}/chat/completions" @@ -112,7 +402,12 @@ def chat( try: with urllib.request.urlopen(req, timeout=self.timeout) as resp: body = json.loads(resp.read().decode("utf-8")) - return self._parse(body) + result = self._parse(body, require_usage=True) + if self.competition_mode: + if result.model != self.model: + raise RuntimeError("Qwen API returned a wrong or missing model") + self.usage_ledger.reconcile(reserved_tokens, result.usage) + return result except urllib.error.HTTPError as exc: # 4xx/5xx detail = exc.read().decode("utf-8", "ignore") last_err = RuntimeError(f"Qwen API HTTP {exc.code}: {detail[:300]}") @@ -121,25 +416,128 @@ def chat( break except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc: last_err = exc + except Exception: + # Response validation errors must not strand a reservation. + # They are not transport failures and should not be retried + # blindly. + self.usage_ledger.release(reserved_tokens) + raise + self.usage_ledger.release(reserved_tokens) raise RuntimeError(f"Qwen chat failed after {self.max_retries + 1} attempts: {last_err}") @staticmethod - def _parse(body: Dict) -> ChatResult: + def _parse(body: Dict, require_usage: bool = False) -> ChatResult: choices = body.get("choices") or [] text = "" if choices: text = (choices[0].get("message") or {}).get("content") or "" - usage_raw = body.get("usage") or {} - usage = TokenUsage( - prompt_tokens=int(usage_raw.get("prompt_tokens", 0)), - completion_tokens=int(usage_raw.get("completion_tokens", 0)), - total_tokens=int(usage_raw.get("total_tokens", 0)), - ) - # Some gateways omit total_tokens; reconstruct so accounting stays sound. - if usage.total_tokens == 0: - usage.total_tokens = usage.prompt_tokens + usage.completion_tokens + usage_raw = body.get("usage") + if require_usage and not isinstance(usage_raw, dict): + raise RuntimeError("Qwen API response is missing usage accounting") + usage_raw = usage_raw or {} + required_usage_fields = {"prompt_tokens", "completion_tokens", "total_tokens"} + if require_usage and not required_usage_fields.issubset(usage_raw): + raise RuntimeError("Qwen API response has incomplete usage accounting") + if require_usage: + values = [usage_raw[name] for name in ("prompt_tokens", "completion_tokens", "total_tokens")] + if any(isinstance(value, bool) or not isinstance(value, int) or value < 0 for value in values): + raise RuntimeError("Qwen API response has invalid usage accounting") + usage = TokenUsage(*values) + if usage.total_tokens != usage.prompt_tokens + usage.completion_tokens: + raise RuntimeError("Qwen API response has inconsistent usage accounting") + else: + usage = TokenUsage() return ChatResult(text=text.strip(), usage=usage, model=str(body.get("model", ""))) + def _validate_competition_provider(self) -> None: + if not isinstance(self.provider, str) or not self.provider.strip(): + raise ValueError("competition mode requires a non-empty provider") + + def _validate_competition_endpoint(self) -> None: + parsed = urlparse(self.base_url) + if ( + parsed.scheme != "https" + or not parsed.hostname + or parsed.port not in (None, 443) + or parsed.params or parsed.query or parsed.fragment + or parsed.username or parsed.password + ): + raise ValueError( + "competition mode requires a valid HTTPS OpenAI-compatible endpoint" + ) + + def _validate_competition_model(self) -> None: + if self.model not in _ALLOWED_QWEN_MODELS: + raise ValueError("competition mode only permits allowlisted Qwen models") + + def assert_formal_configuration(self) -> None: + """Validate the complete formal-mode boundary without using transport. + + This method is intentionally public for the QA agent's dependency gate: + an injected object is accepted only when it is the exact formal Qwen + client and this complete validation succeeds. + """ + if not self.competition_mode: + raise ValueError("formal mode requires competition_mode=True") + self._validate_competition_provider() + self._validate_competition_endpoint() + self._validate_competition_model() + if self.usage_ledger.max_tokens != DEFAULT_TOKEN_BUDGET: + raise ValueError( + f"formal mode requires an exact {DEFAULT_TOKEN_BUDGET} token budget" + ) + if not self.usage_ledger.is_persistent: + raise ValueError("competition mode requires a persistent usage ledger") + if not isinstance(self.formal_run_id, str) or not self.formal_run_id: + raise ValueError("formal mode requires a non-empty formal_run_id") + if self.formal_ledger_path is None: + raise ValueError("formal mode requires an explicit formal_ledger_path") + if self.usage_ledger.path is None or ( + _canonical_ledger_path(str(self.usage_ledger.path)) + != self.formal_ledger_path + ): + raise ValueError( + "formal usage_ledger path must exactly match formal_ledger_path" + ) + _bind_formal_ledger_path(self.formal_run_id, self.formal_ledger_path) + + +def _canonical_ledger_path(path: str) -> str: + if not isinstance(path, str) or not path.strip(): + raise ValueError("formal_ledger_path must be a non-empty path") + return str(Path(path).expanduser().resolve(strict=False)) + + +def _bind_formal_ledger_path(run_id: str, ledger_path: str) -> None: + """Reject a different ledger path for an already configured formal run.""" + with _FORMAL_LEDGER_PATHS_LOCK: + configured_path = _FORMAL_LEDGER_PATHS.setdefault(run_id, ledger_path) + if configured_path != ledger_path: + raise ValueError( + "formal run is already bound to a different formal_ledger_path" + ) + + +def _deny_formal_network_transport() -> None: + """Fail before payload, authorization header, or urllib transport exists.""" + if os.environ.get(_FORMAL_NETWORK_ENV, "0") != "1": + raise RuntimeError("formal Qwen network transport is denied in this offline phase") + + +def formal_network_denied_probe() -> None: + """Exercise only the formal deny gate, without configuration or I/O. + + In particular, this probe does not construct a client, inspect credentials, + create a ledger, acquire a lock, build a request, or call ``urlopen``. + """ + try: + _deny_formal_network_transport() + except RuntimeError as exc: + if "network transport is denied" not in str(exc): + raise AssertionError("formal network probe failed for the wrong reason") from exc + else: + raise AssertionError("formal network probe unexpectedly permitted a call") + class MockChatClient: """Deterministic offline stand-in. @@ -185,24 +583,60 @@ def _estimate_tokens(text: str) -> int: return cjk + max(1, latin // 4) -def build_chat_client(): +def build_chat_client( + competition_mode: Optional[bool] = None, + token_budget: int = DEFAULT_TOKEN_BUDGET, + usage_ledger_path: Optional[str] = None, + formal_run_id: Optional[str] = None, + formal_ledger_path: Optional[str] = None, + *, + test_mode: Optional[bool] = None, +): """Return a chat client based on environment configuration. - Never raises for a missing key: if the ``qwen`` backend is requested but no - API key is available, falls back to :class:`MockChatClient` so the pipeline - still runs (offline-first invariant). + Mock is test-only and must be explicitly selected. Missing credentials, + invalid providers/models/endpoints, and construction errors always raise. """ backend = os.environ.get("AWARELIQUID_LLM_BACKEND", "qwen").lower() + if competition_mode is None: + competition_mode = os.environ.get("AWARELIQUID_COMPETITION_MODE", "0") == "1" + competition_mode = bool(competition_mode) + if test_mode is None: + test_mode = os.environ.get("AWARELIQUID_TEST_MODE", "0") == "1" if backend == "mock": + if competition_mode: + raise RuntimeError("mock backend is forbidden in competition mode") + if not test_mode: + raise RuntimeError("mock backend requires explicit test mode") return MockChatClient() + if backend != "qwen": + raise RuntimeError("only the qwen backend is supported") api_key = os.environ.get("AWARELIQUID_LLM_API_KEY") or os.environ.get("DASHSCOPE_API_KEY", "") if not api_key: - return MockChatClient() + raise RuntimeError("Qwen backend requires AWARELIQUID_LLM_API_KEY") base_url = os.environ.get("AWARELIQUID_LLM_BASE_URL", DEFAULT_BASE_URL) model = os.environ.get("AWARELIQUID_LLM_MODEL", DEFAULT_MODEL) - try: - return QwenChatClient(api_key=api_key, base_url=base_url, model=model) - except Exception: - return MockChatClient() + if competition_mode: + if usage_ledger_path is not None: + raise ValueError( + "formal mode accepts ledger configuration only through formal_ledger_path" + ) + formal_run_id = formal_run_id or os.environ.get("AWARELIQUID_FORMAL_RUN_ID") + formal_ledger_path = formal_ledger_path or os.environ.get( + "AWARELIQUID_FORMAL_LEDGER_PATH" + ) + return QwenChatClient( + api_key=api_key, + base_url=base_url, + model=model, + provider=os.environ.get("AWARELIQUID_LLM_PROVIDER", DEFAULT_PROVIDER), + competition_mode=competition_mode, + token_budget=token_budget, + usage_ledger_path=( + usage_ledger_path or os.environ.get("AWARELIQUID_USAGE_LEDGER_PATH") + ) if not competition_mode else None, + formal_run_id=formal_run_id, + formal_ledger_path=formal_ledger_path, + ) diff --git a/awareliquid/adapter/schemas.py b/awareliquid/adapter/schemas.py index cc36d65..10c48b2 100644 --- a/awareliquid/adapter/schemas.py +++ b/awareliquid/adapter/schemas.py @@ -46,6 +46,16 @@ def parse_answer(raw: str, qtype: str, num_options: int = 4) -> str: raise ValueError(f"unknown qtype {qtype!r}, expected one of {VALID_TYPES}") text = raw or "" + if qtype == "tf": + # Providers occasionally ignore the letter-only instruction and emit + # the semantic boolean. Normalize that safe, unambiguous form before + # scanning prose for option letters (where the ``A`` in ``Answer`` + # would otherwise be a tempting false positive). + boolean = text.strip().lower() + if re.fullmatch(r"(?:true|yes|正确|对)", boolean): + return "A" + if re.fullmatch(r"(?:false|no|错误|错)", boolean): + return "B" # Defence in depth (the prompt already asks for letters only): if the model # still prefixes reasoning, narrow to the span after an explicit answer marker # or, failing that, the last non-empty line -- so acronyms in the reasoning diff --git a/awareliquid/evidence.py b/awareliquid/evidence.py new file mode 100644 index 0000000..212df5c --- /dev/null +++ b/awareliquid/evidence.py @@ -0,0 +1,194 @@ +"""Deterministic, offline provenance records for parsed source material. + +This module deliberately records source boundaries only. It does not retrieve, +rank, summarize, or infer from source text. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from hashlib import sha256 +import json +from typing import Any, Mapping, Optional, Sequence, Tuple + + +PARSER_VERSION = "a3-evidence-v1" + + +REQUIRED_PROVENANCE_FIELDS = ( + "domain", + "doc_id", + "page", + "source_path", + "char_start", + "char_end", + "content", + "section", + "title", + "table_id", + "row_id", + "column_ids", + "unit", + "footnote", + "parent_evidence_id", + "neighbor_evidence_ids", + "parse_warning", +) + + +def _canonical_json(value: Mapping[str, Any]) -> bytes: + return json.dumps( + value, ensure_ascii=False, sort_keys=True, separators=(",", ":"), allow_nan=False + ).encode("utf-8") + + +def _optional_text(value: Optional[str], name: str) -> Optional[str]: + if value is not None and not isinstance(value, str): + raise TypeError(f"{name} must be a string or None") + return value + + +def _text_tuple(value: Sequence[str], name: str) -> Tuple[str, ...]: + if value is None: + raise TypeError(f"{name} must be a sequence of strings") + if isinstance(value, str): + raise TypeError(f"{name} must be a sequence of strings, not a string") + result = tuple(value) + if not all(isinstance(item, str) for item in result): + raise TypeError(f"{name} must contain only strings") + return result + + +@dataclass(frozen=True) +class Evidence: + """A non-semantic record of an extracted source span and its boundaries. + + ``content`` is the exact text emitted by a parser for this record. The + offsets remain offsets in the original parser source, rather than offsets + into ``content``. Consequently, a record can preserve a table row or a + page fragment without changing its original location. + """ + + domain: str + doc_id: str + page: int + source_path: str + char_start: int + char_end: int + content: str + section: Optional[str] = None + title: Optional[str] = None + table_id: Optional[str] = None + row_id: Optional[str] = None + column_ids: Tuple[str, ...] = () + unit: Optional[str] = None + footnote: Optional[str] = None + parent_evidence_id: Optional[str] = None + neighbor_evidence_ids: Tuple[str, ...] = () + parse_warning: Tuple[str, ...] = () + parser_version: str = PARSER_VERSION + evidence_id: str = field(init=False) + + def __post_init__(self) -> None: + for name in ("domain", "doc_id", "source_path", "parser_version"): + value = getattr(self, name) + if not isinstance(value, str) or not value: + raise ValueError(f"{name} must be a non-empty string") + if not isinstance(self.content, str): + raise TypeError("content must be a string") + if isinstance(self.page, bool) or not isinstance(self.page, int) or self.page < 1: + raise ValueError("page must be a positive integer") + if any(isinstance(value, bool) or not isinstance(value, int) for value in (self.char_start, self.char_end)): + raise TypeError("char_start and char_end must be integers") + if self.char_start < 0 or self.char_end < self.char_start: + raise ValueError("offsets must satisfy 0 <= char_start <= char_end") + + for name in ("section", "title", "table_id", "row_id", "unit", "footnote", "parent_evidence_id"): + object.__setattr__(self, name, _optional_text(getattr(self, name), name)) + for name in ("column_ids", "neighbor_evidence_ids", "parse_warning"): + object.__setattr__(self, name, _text_tuple(getattr(self, name), name)) + + object.__setattr__(self, "evidence_id", self.stable_id()) + + @property + def content_sha256(self) -> str: + """SHA-256 of the exact extracted content, encoded as UTF-8.""" + + return sha256(self.content.encode("utf-8")).hexdigest() + + def stable_id(self) -> str: + """Return the ID derived only from version, source, position, and text.""" + + identity = { + "char_end": self.char_end, + "char_start": self.char_start, + "content_sha256": self.content_sha256, + "doc_id": self.doc_id, + "page": self.page, + "parser_version": self.parser_version, + "source_path": self.source_path, + } + return "evidence-" + sha256(_canonical_json(identity)).hexdigest() + + def to_dict(self) -> dict[str, Any]: + """Serialize every provenance and boundary field without omission.""" + + return { + "evidence_id": self.evidence_id, + "parser_version": self.parser_version, + "domain": self.domain, + "doc_id": self.doc_id, + "page": self.page, + "source_path": self.source_path, + "char_start": self.char_start, + "char_end": self.char_end, + "content": self.content, + "content_sha256": self.content_sha256, + "section": self.section, + "title": self.title, + "table_id": self.table_id, + "row_id": self.row_id, + "column_ids": list(self.column_ids), + "unit": self.unit, + "footnote": self.footnote, + "parent_evidence_id": self.parent_evidence_id, + "neighbor_evidence_ids": list(self.neighbor_evidence_ids), + "parse_warning": list(self.parse_warning), + } + + @classmethod + def from_dict(cls, record: Mapping[str, Any]) -> "Evidence": + """Normalize a parsed record while rejecting absent required provenance.""" + + missing = [name for name in REQUIRED_PROVENANCE_FIELDS if name not in record] + if missing: + raise ValueError(f"missing required evidence fields: {', '.join(missing)}") + return cls( + domain=record["domain"], + doc_id=record["doc_id"], + page=record["page"], + source_path=record["source_path"], + char_start=record["char_start"], + char_end=record["char_end"], + content=record["content"], + section=record.get("section"), + title=record.get("title"), + table_id=record.get("table_id"), + row_id=record.get("row_id"), + column_ids=record.get("column_ids"), + unit=record.get("unit"), + footnote=record.get("footnote"), + parent_evidence_id=record.get("parent_evidence_id"), + neighbor_evidence_ids=record.get("neighbor_evidence_ids"), + parse_warning=record.get("parse_warning"), + parser_version=record.get("parser_version", PARSER_VERSION), + ) + + +EvidenceRecord = Evidence + + +def normalize_evidence(record: Mapping[str, Any]) -> Evidence: + """Build one deterministic :class:`Evidence` value from parser output.""" + + return Evidence.from_dict(record) diff --git a/awareliquid/formal_reasoning.py b/awareliquid/formal_reasoning.py new file mode 100644 index 0000000..23cb1ff --- /dev/null +++ b/awareliquid/formal_reasoning.py @@ -0,0 +1,291 @@ +"""Offline structural evidence handling for the A4 contract.""" + +from __future__ import annotations + +from dataclasses import dataclass +from decimal import Decimal, InvalidOperation +from enum import Enum +from typing import Iterable, Optional, Sequence, Tuple, Union + +from .evidence import Evidence + + +DecimalInput = Union[Decimal, int, str] +_ALLOWED_ARITHMETIC = {"add", "subtract", "multiply", "divide"} +_ALLOWED_COMPARISONS = { + "equal", "not_equal", "greater_than", "greater_or_equal", "less_than", "less_or_equal" +} +_TABLE_FIELD_NAMES = {"table_id", "row_id", "column_ids", "unit", "footnote"} + + +def _nonempty_text(value: str, name: str) -> str: + if not isinstance(value, str) or not value: + raise ValueError(f"{name} must be a non-empty string") + return value + + +def _text_tuple(value: Sequence[str], name: str, *, required: bool = False) -> Tuple[str, ...]: + if isinstance(value, str) or value is None: + raise TypeError(f"{name} must be a sequence of strings") + result = tuple(value) + if required and not result: + raise ValueError(f"{name} must not be empty") + if not all(isinstance(item, str) and item for item in result): + raise ValueError(f"{name} must contain non-empty strings") + return result + + +def _decimal(value: DecimalInput) -> Decimal: + if isinstance(value, bool) or isinstance(value, float): + raise TypeError("decimal values must be Decimal, int, or string") + if not isinstance(value, (Decimal, int, str)): + raise TypeError("decimal values must be Decimal, int, or string") + try: + result = Decimal(value) + except (InvalidOperation, ValueError) as error: + raise ValueError("invalid decimal value") from error + if not result.is_finite(): + raise ValueError("decimal value must be finite") + return result + + +def _merge_ids(values: Iterable["EvidenceDecimal"]) -> Tuple[str, ...]: + return tuple(dict.fromkeys(identifier for value in values for identifier in value.evidence_ids)) + + +@dataclass(frozen=True) +class EvidenceContext: + """An ordered, non-transforming collection of source Evidence records.""" + + records: Tuple[Evidence, ...] + + def __init__(self, records: Sequence[Evidence]): + result = tuple(records) + if not all(isinstance(record, Evidence) for record in result): + raise TypeError("records must contain Evidence values") + identifiers = tuple(record.evidence_id for record in result) + if len(set(identifiers)) != len(identifiers): + raise ValueError("records must have unique evidence IDs") + object.__setattr__(self, "records", result) + + @property + def evidence_ids(self) -> Tuple[str, ...]: + return tuple(record.evidence_id for record in self.records) + + def require_ids(self, evidence_ids: Sequence[str]) -> Tuple[str, ...]: + identifiers = _text_tuple(evidence_ids, "evidence_ids", required=True) + identifiers = tuple(dict.fromkeys(identifiers)) + known = set(self.evidence_ids) + unknown = [identifier for identifier in identifiers if identifier not in known] + if unknown: + raise ValueError(f"unknown evidence IDs: {', '.join(unknown)}") + return identifiers + + def select(self, evidence_ids: Sequence[str]) -> "EvidenceContext": + wanted = set(self.require_ids(evidence_ids)) + return EvidenceContext(tuple(record for record in self.records if record.evidence_id in wanted)) + + def to_dicts(self) -> Tuple[dict, ...]: + return tuple(record.to_dict() for record in self.records) + + +@dataclass(frozen=True) +class EvidenceDecimal: + """One finite Decimal value explicitly tied to source Evidence IDs.""" + + value: Decimal + evidence_ids: Tuple[str, ...] + context: EvidenceContext + unit: Optional[str] = None + + def __init__( + self, + value: DecimalInput, + evidence_ids: Sequence[str], + context: EvidenceContext, + unit: Optional[str] = None, + ): + if not isinstance(context, EvidenceContext): + raise TypeError("context must be an EvidenceContext") + if unit is not None and (not isinstance(unit, str) or not unit): + raise ValueError("unit must be a non-empty string or None") + object.__setattr__(self, "value", _decimal(value)) + object.__setattr__(self, "evidence_ids", context.require_ids(evidence_ids)) + object.__setattr__(self, "context", context) + object.__setattr__(self, "unit", unit) + + +def structural_arithmetic(operation: str, operands: Sequence[EvidenceDecimal]) -> EvidenceDecimal: + """Apply one whitelisted Decimal operation while retaining all provenance.""" + + if operation not in _ALLOWED_ARITHMETIC: + raise ValueError("unknown structural arithmetic operation") + values = tuple(operands) + if not values or not all(isinstance(value, EvidenceDecimal) for value in values): + raise TypeError("operands must be a non-empty sequence of EvidenceDecimal values") + if operation in {"subtract", "divide"} and len(values) != 2: + raise ValueError(f"{operation} requires exactly two operands") + if operation == "add": + result = sum((value.value for value in values), Decimal("0")) + elif operation == "multiply": + result = Decimal("1") + for value in values: + result *= value.value + elif operation == "subtract": + result = values[0].value - values[1].value + else: + if values[1].value == 0: + raise ZeroDivisionError("division by zero") + result = values[0].value / values[1].value + unit = values[0].unit if all(value.unit == values[0].unit for value in values) else None + records = {record.evidence_id: record for value in values for record in value.context.records} + return EvidenceDecimal(result, _merge_ids(values), EvidenceContext(tuple(records.values())), unit) + + +class OptionState(str, Enum): + SUPPORTED = "supported" + REFUTED = "refuted" + INSUFFICIENT = "insufficient" + + +@dataclass(frozen=True) +class OptionDecision: + option_id: str + state: OptionState + evidence_ids: Tuple[str, ...] + comparison: Optional[str] + + +def _compare(actual: Decimal, expected: Decimal, comparison: str) -> bool: + return { + "equal": actual == expected, + "not_equal": actual != expected, + "greater_than": actual > expected, + "greater_or_equal": actual >= expected, + "less_than": actual < expected, + "less_or_equal": actual <= expected, + }[comparison] + + +def evaluate_option( + option_id: str, + actual: Optional[EvidenceDecimal], + expected: Optional[EvidenceDecimal], + comparison: str = "equal", + *, + observed_text: Optional[str] = None, + required_text: Optional[str] = None, +) -> OptionDecision: + """Return a deterministic state for a bound numerical and exact-text claim.""" + + _nonempty_text(option_id, "option_id") + if comparison not in _ALLOWED_COMPARISONS: + raise ValueError("unknown comparison") + values = tuple(value for value in (actual, expected) if value is not None) + if not all(isinstance(value, EvidenceDecimal) for value in values): + raise TypeError("actual and expected must be EvidenceDecimal values or None") + evidence_ids = _merge_ids(values) + if actual is None or expected is None or actual.unit != expected.unit: + return OptionDecision(option_id, OptionState.INSUFFICIENT, evidence_ids, comparison) + if (observed_text is None) != (required_text is None): + return OptionDecision(option_id, OptionState.INSUFFICIENT, evidence_ids, comparison) + if observed_text is not None and (not isinstance(observed_text, str) or not isinstance(required_text, str)): + raise TypeError("text boundaries must be strings or None") + matches = _compare(actual.value, expected.value, comparison) + matches = matches and (observed_text is None or observed_text == required_text) + state = OptionState.SUPPORTED if matches else OptionState.REFUTED + return OptionDecision(option_id, state, evidence_ids, comparison) + + +@dataclass(frozen=True) +class ExactQuery: + """Only exact structural fields accepted by ExactLocator.""" + + split: str + doc_ids: Tuple[str, ...] = () + official_b_data: bool = False + doc_id: Optional[str] = None + title: Optional[str] = None + section: Optional[str] = None + clause: Optional[str] = None + phrase: Optional[str] = None + numbers: Tuple[str, ...] = () + dates: Tuple[str, ...] = () + percentages: Tuple[str, ...] = () + currencies: Tuple[str, ...] = () + ratings: Tuple[str, ...] = () + table_fields: Tuple[Tuple[str, str], ...] = () + char_start: Optional[int] = None + char_end: Optional[int] = None + + def __post_init__(self) -> None: + if self.split not in {"A", "B"}: + raise ValueError("split must be A or B") + doc_ids = _text_tuple(self.doc_ids, "doc_ids") + if self.split == "A" and not doc_ids: + raise ValueError("A queries require non-empty doc_ids") + if self.split == "B" and not self.official_b_data: + raise ValueError("B queries require official B data") + if self.split == "B" and doc_ids: + raise ValueError("B queries must not supply doc_ids") + object.__setattr__(self, "doc_ids", tuple(dict.fromkeys(doc_ids))) + for name in ("doc_id", "title", "section", "clause", "phrase"): + value = getattr(self, name) + if value is not None: + object.__setattr__(self, name, _nonempty_text(value, name)) + for name in ("numbers", "dates", "percentages", "currencies", "ratings"): + object.__setattr__(self, name, _text_tuple(getattr(self, name), name)) + if (self.char_start is None) != (self.char_end is None): + raise ValueError("char_start and char_end must be supplied together") + if self.char_start is not None: + if any(isinstance(value, bool) or not isinstance(value, int) for value in (self.char_start, self.char_end)): + raise TypeError("character bounds must be integers") + if self.char_start < 0 or self.char_end < self.char_start: + raise ValueError("character bounds must satisfy 0 <= start <= end") + fields = tuple(self.table_fields) + for name, value in fields: + if name not in _TABLE_FIELD_NAMES or not isinstance(value, str) or not value: + raise ValueError("table_fields must contain allowed exact field/value pairs") + object.__setattr__(self, "table_fields", fields) + + +class ExactLocator: + """Locate records by conjunction of exact allowed field checks only.""" + + def __init__(self, context: EvidenceContext): + if not isinstance(context, EvidenceContext): + raise TypeError("context must be an EvidenceContext") + self._context = context + + def locate(self, query: ExactQuery) -> Tuple[str, ...]: + if not isinstance(query, ExactQuery): + raise TypeError("query must be an ExactQuery") + matches = [record for record in self._context.records if self._matches(record, query)] + matches.sort(key=lambda record: (record.doc_id, record.page, record.char_start, record.char_end, record.evidence_id)) + return tuple(record.evidence_id for record in matches) + + @staticmethod + def _matches(record: Evidence, query: ExactQuery) -> bool: + if query.split == "A" and record.doc_id not in query.doc_ids: + return False + if query.doc_id is not None and record.doc_id != query.doc_id: + return False + if query.title is not None and record.title != query.title: + return False + if query.section is not None and record.section != query.section: + return False + if query.clause is not None and record.section != query.clause: + return False + if query.phrase is not None and query.phrase not in record.content: + return False + tokens = (*query.numbers, *query.dates, *query.percentages, *query.currencies, *query.ratings) + if any(token not in record.content for token in tokens): + return False + if query.char_start is not None and (record.char_start != query.char_start or record.char_end != query.char_end): + return False + return all(ExactLocator._table_value_matches(record, name, value) for name, value in query.table_fields) + + @staticmethod + def _table_value_matches(record: Evidence, name: str, value: str) -> bool: + field_value = getattr(record, name) + return value in field_value if name == "column_ids" else field_value == value diff --git a/awareliquid/hashing.py b/awareliquid/hashing.py new file mode 100644 index 0000000..a3698ff --- /dev/null +++ b/awareliquid/hashing.py @@ -0,0 +1,16 @@ +"""Salt-free text hashing for scoring paths. + +内建 ``hash()`` 的盐随进程变化（PYTHONHASHSEED），出现在检索/打分路径上 +会让结果跨进程不可复现——2026-09-06 bench 数字漂移事故的根因（见 +docs/RESULTS.md 对账日志与 docs/PREREGISTRATION.md 不做清单）。所有需要 +"字符串 → 稳定桶"的地方一律走本模块，禁止回退到内建 ``hash()``。 +""" + +from __future__ import annotations + +import hashlib + + +def stable_hash(text: str) -> int: + """Deterministic 32-bit bucket hash — identical across processes and salt settings.""" + return int(hashlib.md5(text.encode("utf-8")).hexdigest()[:8], 16) diff --git a/awareliquid/memory/__init__.py b/awareliquid/memory/__init__.py index 0b49f8c..eb7d4f4 100644 --- a/awareliquid/memory/__init__.py +++ b/awareliquid/memory/__init__.py @@ -13,8 +13,10 @@ from .knowledge_store import PersistentKnowledgeMemory from .encoder import SentenceEncoder +from .lexical_store import LexicalKnowledgeMemory __all__ = [ "PersistentKnowledgeMemory", "SentenceEncoder", + "LexicalKnowledgeMemory", ] diff --git a/awareliquid/memory/knowledge_store.py b/awareliquid/memory/knowledge_store.py index bc8f82e..c5baadd 100644 --- a/awareliquid/memory/knowledge_store.py +++ b/awareliquid/memory/knowledge_store.py @@ -268,9 +268,12 @@ def query( # Restrict the candidate set to the given documents BEFORE scoring, so # ranking (and centering) happen within the allowed set rather than # globally-then-filtered -- which would drop allowed chunks that rank - # below the global top-k. An empty/None list means "all documents". + # below the global top-k. None means "all documents"; an explicit empty + # list means "no documents". with self._lock: - if doc_ids: + if doc_ids is not None and not doc_ids: + return [] + if doc_ids is not None: placeholders = ",".join("?" * len(doc_ids)) rows = self._conn.execute( f"SELECT id, key_vec, content, meta FROM knowledge " @@ -369,7 +372,9 @@ def search_bm25( return [] sql = "SELECT rowid, content FROM knowledge_fts WHERE knowledge_fts MATCH ?" params: List[Any] = [match] - if doc_ids: + if doc_ids is not None and not doc_ids: + return [] + if doc_ids is not None: placeholders = ",".join("?" * len(doc_ids)) sql += f" AND doc_id IN ({placeholders})" params += [str(d) for d in doc_ids] @@ -399,6 +404,20 @@ def fts_enabled(self) -> bool: """Whether the lexical (FTS5/BM25) channel is available in this build.""" return self._fts_enabled + def available_doc_ids(self) -> List[str]: + """Return document ids currently represented in the store.""" + with self._lock: + rows = self._conn.execute( + "SELECT DISTINCT doc_id FROM knowledge " + "WHERE doc_id IS NOT NULL ORDER BY doc_id" + ).fetchall() + return [str(row[0]) for row in rows] + + def has_doc_ids(self, doc_ids: Sequence[str]) -> bool: + """Return whether every requested document id exists in the store.""" + wanted = {str(doc_id) for doc_id in doc_ids} + return wanted.issubset(set(self.available_doc_ids())) + # ------------------------------------------------------------------ # Bookkeeping # ------------------------------------------------------------------ diff --git a/awareliquid/memory/lexical_store.py b/awareliquid/memory/lexical_store.py new file mode 100644 index 0000000..51e17da --- /dev/null +++ b/awareliquid/memory/lexical_store.py @@ -0,0 +1,362 @@ +"""SQLite/FTS5 lexical store for the competition-safe retrieval path. + +This module deliberately has no torch, transformers, or embedding dependency. +It is the store used by ``MemoryQAAgent`` when ``retrieval_backend="lexical"`` +is selected. The older vector store remains available for research and +backward-compatible tests, but the formal competition path must use this one. +""" + +from __future__ import annotations + +import json +import re +import sqlite3 +import threading +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + + +_SCHEMA = """ +CREATE TABLE IF NOT EXISTS records ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + content TEXT NOT NULL, + meta TEXT, + doc_id TEXT, + search_text TEXT NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_records_doc_id ON records(doc_id); +""" +_FIELD_NAMES = ( + "content", + "title", + "section", + "clause_id", + "table_id", + "row_id", + "column_ids", + "keywords", + "anchors", + "search_terms", + "unit", +) +_FIELD_WEIGHTS = (1.0, 5.0, 4.0, 6.0, 5.0, 5.0, 3.0, 3.0, 6.0, 4.0, 2.0) +_STRUCTURAL_ANCHOR = re.compile( + r"(?:19|20)\d{2}\s*年?" + r"|\d+(?:,\d{3})*(?:\.\d+)?\s*" + r"(?:个百分点|亿元|万元|美元|港元|bp|BP|%|％|亿|万|元)" + r"|第\s*[一二三四五六七八九十百\d]+\s*" + r"(?:条|章|节|款|项|个保单年度|保单年度)" + r"|AAA|AA\+?" +) + + +def _json_dump(value: Any) -> Optional[str]: + if value is None: + return None + return json.dumps(value, ensure_ascii=False, default=str, sort_keys=True) + + +def _json_load(value: Optional[str]) -> Any: + if value is None: + return None + return json.loads(value) + + +def _cjk_ngrams(run: str) -> List[str]: + terms: List[str] = [] + if len(run) >= 2: + terms.extend(run[i : i + 2] for i in range(len(run) - 1)) + if len(run) >= 3: + terms.extend(run[i : i + 3] for i in range(len(run) - 2)) + return terms + + +def _search_text(content: str, meta: Optional[Any]) -> str: + """Expand CJK bigrams and metadata into FTS5-searchable tokens.""" + parts = [content or ""] + if isinstance(meta, dict): + for key in ( + "title", + "section", + "clause_id", + "page", + "table_id", + "row_id", + "column_ids", + "keywords", + "anchors", + "search_terms", + "unit", + ): + value = meta.get(key) + if value is not None: + parts.append(str(value)) + raw = " ".join(parts) + expanded: List[str] = [raw] + for run in re.findall(r"[一-鿿]+", raw): + expanded.extend(_cjk_ngrams(run)) + return " ".join(expanded) + + +def _field_texts(content: str, meta: Optional[Any]) -> Dict[str, str]: + values = {name: "" for name in _FIELD_NAMES} + values["content"] = content or "" + if isinstance(meta, dict): + values["title"] = str(meta.get("title") or meta.get("doc_id") or "") + for name in _FIELD_NAMES[2:]: + value = meta.get(name) + if value is not None: + values[name] = str(value) + return values + + +def _expand_field(value: str) -> str: + raw = value or "" + expanded = [raw] + for run in re.findall(r"[一-鿿]+", raw): + expanded.extend(_cjk_ngrams(run)) + return " ".join(expanded) + + +def _fts_schema() -> str: + fields = ", ".join(_FIELD_NAMES) + return ( + "CREATE VIRTUAL TABLE IF NOT EXISTS records_fts_fields " + f"USING fts5({fields}, doc_id UNINDEXED, tokenize='unicode61')" + ) + + +def _query_terms(text: str) -> List[str]: + terms: List[str] = [] + for word in re.findall(r"[A-Za-z0-9][A-Za-z0-9.%_/-]*", text or ""): + if len(word) >= 2: + terms.append(word.lower()) + for run in re.findall(r"[一-鿿]+", text or ""): + terms.extend(_cjk_ngrams(run)) + seen = set() + unique: List[str] = [] + for term in terms: + if term not in seen: + seen.add(term) + unique.append(term) + return unique[:96] + + +def _structural_bonus(query_text: str, content: str, meta: Optional[Any]) -> int: + """Prefer BM25 hits containing exact high-signal query anchors.""" + haystack = _search_text(content, meta).replace(" ", "").lower() + return sum( + 1 + for anchor in _STRUCTURAL_ANCHOR.findall(query_text or "") + if anchor.replace(" ", "").lower() in haystack + ) + + +class LexicalKnowledgeMemory: + """Small local store with BM25-style FTS5 retrieval and strict doc filters.""" + + def __init__(self, db_path: Union[str, Path] = ":memory:"): + self.db_path = str(db_path) + self._conn = sqlite3.connect(self.db_path, check_same_thread=False) + self._conn.executescript(_SCHEMA) + self._conn.commit() + self._lock = threading.RLock() + self._fts_enabled = False + try: + self._conn.execute( + "CREATE VIRTUAL TABLE IF NOT EXISTS records_fts " + "USING fts5(search_text, doc_id UNINDEXED, tokenize='unicode61')" + ) + self._conn.execute(_fts_schema()) + self._conn.commit() + self._fts_enabled = True + except sqlite3.OperationalError: + self._fts_enabled = False + + # Rebuild the FTS mirror if a database was copied without the virtual + # table rows. This is intentionally deterministic and local-only. + if self._fts_enabled: + fts_count = int( + self._conn.execute("SELECT COUNT(*) FROM records_fts_fields").fetchone()[0] + ) + record_count = len(self) + if fts_count != record_count: + self._conn.execute("DELETE FROM records_fts") + self._conn.execute("DELETE FROM records_fts_fields") + rows = self._conn.execute("SELECT id, search_text, doc_id FROM records").fetchall() + self._conn.executemany( + "INSERT INTO records_fts(rowid, search_text, doc_id) VALUES (?, ?, ?)", + rows, + ) + for row_id, _searchable, doc_id in rows: + row = self._conn.execute( + "SELECT content, meta FROM records WHERE id = ?", (int(row_id),) + ).fetchone() + if row is None: + continue + content, meta = str(row[0]), _json_load(row[1]) + fields = _field_texts(content, meta) + values = [_expand_field(fields[name]) for name in _FIELD_NAMES] + self._conn.execute( + "INSERT INTO records_fts_fields(rowid, " + + ", ".join(_FIELD_NAMES) + + ", doc_id) VALUES (" + + ", ".join("?" for _ in range(len(_FIELD_NAMES) + 2)) + + ")", + [int(row_id), *values, doc_id], + ) + self._conn.commit() + + def write(self, content: str, meta: Optional[Dict[str, Any]] = None) -> int: + if not isinstance(content, str) or not content.strip(): + raise ValueError("content must be a non-empty string") + doc_id = None + if isinstance(meta, dict) and meta.get("doc_id") is not None: + doc_id = str(meta["doc_id"]) + metadata = _json_dump(meta) + searchable = _search_text(content, meta) + fields = _field_texts(content, meta) + with self._lock: + cur = self._conn.execute( + "INSERT INTO records(content, meta, doc_id, search_text) VALUES (?, ?, ?, ?)", + (content, metadata, doc_id, searchable), + ) + row_id = int(cur.lastrowid) + if self._fts_enabled: + self._conn.execute( + "INSERT INTO records_fts(rowid, search_text, doc_id) VALUES (?, ?, ?)", + (row_id, searchable, doc_id), + ) + values = [_expand_field(fields[name]) for name in _FIELD_NAMES] + self._conn.execute( + "INSERT INTO records_fts_fields(rowid, " + + ", ".join(_FIELD_NAMES) + + ", doc_id) VALUES (" + + ", ".join("?" for _ in range(len(_FIELD_NAMES) + 2)) + + ")", + [row_id, *values, doc_id], + ) + self._conn.commit() + return row_id + + def search_bm25( + self, + query_text: str, + top_k: int = 10, + doc_ids: Optional[Sequence[str]] = None, + ) -> List[Tuple[int, str, Optional[Dict[str, Any]]]]: + if top_k <= 0: + raise ValueError("top_k must be positive") + if doc_ids is not None and not doc_ids: + return [] + if not self._fts_enabled: + return [] + terms = _query_terms(query_text) + if not terms: + return [] + match = " OR ".join('"' + term.replace('"', '""') + '"' for term in terms) + sql = "SELECT rowid FROM records_fts_fields WHERE records_fts_fields MATCH ?" + params: List[Any] = [match] + if doc_ids is not None: + placeholders = ",".join("?" * len(doc_ids)) + sql += f" AND doc_id IN ({placeholders})" + params.extend(str(doc_id) for doc_id in doc_ids) + sql += ( + " ORDER BY bm25(records_fts_fields, " + + ", ".join(str(weight) for weight in _FIELD_WEIGHTS) + + ") LIMIT ?" + ) + params.append(max(int(top_k) * 8, 32)) + with self._lock: + try: + rows = self._conn.execute(sql, params).fetchall() + except sqlite3.OperationalError: + return [] + results: List[Tuple[int, str, Optional[Dict[str, Any]]]] = [] + for (row_id,) in rows: + row = self._conn.execute( + "SELECT content, meta FROM records WHERE id = ?", (int(row_id),) + ).fetchone() + if row is None: + continue + results.append((int(row_id), str(row[0]), _json_load(row[1]))) + ranked = sorted( + enumerate(results), + key=lambda item: ( + -_structural_bonus(query_text, item[1][1], item[1][2]), + item[0], + ), + ) + return [item[1] for item in ranked[:top_k]] + + def expand_neighbors( + self, + hits: List[Tuple[int, str, Optional[Dict[str, Any]]]], + max_extra: int = 2, + ) -> List[Tuple[int, str, Optional[Dict[str, Any]]]]: + """Add nearby source nodes without changing BM25 ranking.""" + if max_extra <= 0 or not hits: + return hits + seen = {int(row_id) for row_id, _content, _meta in hits} + expanded = list(hits) + with self._lock: + for _row_id, _content, meta in hits: + if not isinstance(meta, dict): + continue + doc_id = meta.get("doc_id") + neighbor_indexes = meta.get("neighbor_chunk_idxs") or [] + for chunk_idx in neighbor_indexes: + if len(expanded) - len(hits) >= max_extra: + return expanded + if doc_id is None: + continue + rows = self._conn.execute( + "SELECT id, content, meta FROM records WHERE doc_id = ?", + (str(doc_id),), + ).fetchall() + for row in rows: + row_meta = _json_load(row[2]) + if not isinstance(row_meta, dict) or row_meta.get("chunk_idx") != int(chunk_idx): + continue + if int(row[0]) in seen: + break + seen.add(int(row[0])) + expanded.append((int(row[0]), str(row[1]), row_meta)) + break + return expanded + + def available_doc_ids(self) -> List[str]: + with self._lock: + rows = self._conn.execute( + "SELECT DISTINCT doc_id FROM records WHERE doc_id IS NOT NULL ORDER BY doc_id" + ).fetchall() + return [str(row[0]) for row in rows] + + def has_doc_ids(self, doc_ids: Sequence[str]) -> bool: + wanted = {str(doc_id) for doc_id in doc_ids} + return wanted.issubset(set(self.available_doc_ids())) + + @property + def fts_enabled(self) -> bool: + return self._fts_enabled + + def __len__(self) -> int: + with self._lock: + return int(self._conn.execute("SELECT COUNT(*) FROM records").fetchone()[0]) + + def clear(self) -> None: + with self._lock: + self._conn.execute("DELETE FROM records") + if self._fts_enabled: + self._conn.execute("DELETE FROM records_fts") + self._conn.execute("DELETE FROM records_fts_fields") + self._conn.commit() + + def close(self) -> None: + self._conn.close() + + def __enter__(self) -> "LexicalKnowledgeMemory": + return self + + def __exit__(self, *_: Any) -> None: + self.close() diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/benchmarks/bench_adapter.py b/benchmarks/bench_adapter.py index 4037510..f9c91f5 100644 --- a/benchmarks/bench_adapter.py +++ b/benchmarks/bench_adapter.py @@ -34,6 +34,7 @@ from awareliquid.adapter.qa_agent import MemoryQAAgent, RetrievalConfig from awareliquid.adapter.qwen_client import MockChatClient, _estimate_tokens +from awareliquid.hashing import stable_hash from awareliquid.memory.knowledge_store import PersistentKnowledgeMemory # -------------------------------------------------------------------------- @@ -121,7 +122,11 @@ def encode(self, text: str, is_query: bool = False) -> torch.Tensor: t = (text or "").lower() for n in (2, 3): # char bigrams + trigrams for i in range(len(t) - n + 1): - vec[hash(t[i : i + n]) % self._dim] += 1.0 + # stable_hash (NOT built-in hash()): the salt of built-in hash() + # is randomized per process (PYTHONHASHSEED), so the same n-gram + # landed in different buckets across runs -- vectors, retrieval + # rankings and every metric below differed between processes. + vec[stable_hash(t[i : i + n]) % self._dim] += 1.0 norm = torch.linalg.vector_norm(vec) return vec / norm if norm > 0 else vec diff --git a/benchmarks/experiment_protocol.py b/benchmarks/experiment_protocol.py new file mode 100644 index 0000000..d65475f --- /dev/null +++ b/benchmarks/experiment_protocol.py @@ -0,0 +1,229 @@ +"""experiment_protocol.py — E0 实验协议固化. + +背景：本项目的对外数字（README bench 表格、评测分数）此前均为单次运行 +产物——lexical stand-in 的 recall 5/6、retention 5/6 都是单跑一遍的计数， +没有 seed 数、显著性或分布形态约束。套用 M1 仓库（MT-LNN）被"单 seed +结论"咬过两次后固化的 E0 纪律：**凡对外引用的对照数字，必须过代码化 +的协议门；过不了门的结果只能入档，不能引用。** + +四条硬纪律（全部零依赖，纯 python + math）： + +1. ``require_min_seeds(per_seed_values, min_seeds=3)`` + 不足 N seeds → ProtocolViolation（引用前必须过这关）。 +2. ``bimodality_1d(values)`` — 双峰检测：1-D 二分聚类， + 报告 {bimodal, split_at, gap_ratio}。双峰数据**不得引用**。 +3. ``paired_sign_test(a, b)`` / ``fisher_exact_2x2(table)`` — 配对/列联 + 检验，不依赖 scipy，跨环境可复现。 +4. ``publishable(per_seed_a, per_seed_b)`` — 引用门槛一键判定： + ≥3 seeds、双臂非双峰、配对检验 p<0.05，返回 (ok, report)。 + +单次运行的 bench 计数（如 6 题召回 6/6）属于**方向筛选**：只能用于 +smoke 和回归对照，不得作为对外结论引用；要引用就必须扩成多 seed / +多题集并过 ``publishable``。 + +用法:: + + from benchmarks.experiment_protocol import publishable + ok, report = publishable(recall_a, recall_b, tag="bench-e5-vs-lexical") + # ok=False 时 report["reasons"] 说明差哪关 —— 结果只能入档，不能引用 +""" + +from __future__ import annotations + +import math +from typing import Dict, List, Sequence, Tuple + + +class ProtocolViolation(RuntimeError): + """违反 E0 协议（seeds 不足 / 双峰区数据 / 引用门槛未过）。""" + + +# 双峰判定的绝对间隙下限（指标量纲按 [0,1] 设计；其他量纲需按比例调整）。 +# 只有比值判据不够：per-seed 分数是量化值（如 recall 只能取 6/8、7/8）， +# 簇内一旦全是重复值，类内散度就是 0，任意两个不同取值的 gap_ratio 都会 +# 爆炸——[0.75, 0.75, 0.75, 0.875] 这种正常一致性数据会被误判成双峰。 +# 要求空带至少跨 0.25，才能谈"两团中间隔着一条空带"。 +_MIN_BIMODAL_GAP = 0.25 + + +def fisher_exact_2x2(table: Sequence[Sequence[int]]) -> Dict: + """2×2 Fisher 精确检验（双侧, hypergeometric 尾概率和法）。""" + ((a, b), (c, d)) = table + n = a + b + c + d + if min(a + b, c + d, a + c, b + d) == 0: + return {"p": 1.0, "odds_ratio": float("nan")} + + def p_exact(x): + return (math.comb(a + c, x) * math.comb(b + d, a + b - x) + / math.comb(n, a + b)) + + p_obs = p_exact(a) + # 双侧: 所有 ≤ 观察概率的表求和（相对容差：超大表下绝对容差会因 + # 浮点末位差漏加数学上相等的表） + lo = max(0, (a + b) - (b + d)) + hi = min(a + b, a + c) + p = sum(p_exact(x) for x in range(lo, hi + 1) + if p_exact(x) <= p_obs * (1 + 1e-9) + 1e-15) + or_ = (a * d) / (b * c) if b and c else float("inf") + return {"p": min(1.0, p), "odds_ratio": or_} + + +def paired_sign_test(a: Sequence[float], b: Sequence[float]) -> Dict: + """配对符号检验（精确二项, 双侧）。 + + H0: P(a_i > b_i) = 0.5。并列对丢弃。含 NaN 的对会被静默当平局 + （NaN 的比较恒为 False）——引用路径上先过 require_min_seeds（拒绝 + NaN/None）再进来。无 scipy 依赖 —— 跨 macOS/Linux/CI 环境结果一致。 + """ + if len(a) != len(b): + raise ValueError("paired_sign_test: a/b 长度不一致") + wins = sum(1 for x, y in zip(a, b) if x > y) + losses = sum(1 for x, y in zip(a, b) if x < y) + n = wins + losses + if n == 0: + return {"p": 1.0, "wins": 0, "losses": 0, "n_effective": 0} + k = min(wins, losses) + p = sum(math.comb(n, i) for i in range(0, k + 1)) / 2 ** n * 2 + return {"p": min(1.0, p), "wins": wins, "losses": losses, + "n_effective": n} + + +def require_min_seeds(values: Sequence[float], min_seeds: int = 3, + what: str = "result") -> List[float]: + vals = [] + for v in values: + if v is None: + # None 必须在 float() 之前拦下，否则会以 TypeError 崩溃而非按协议判负 + raise ProtocolViolation(f"{what}: per-seed 值含 None") + vals.append(float(v)) + if len(vals) < min_seeds: + raise ProtocolViolation( + f"{what}: {len(vals)} seed(s) < required {min_seeds} — " + "E0 协议禁止引用单/双 seed 结论") + if any(math.isnan(v) for v in vals): + raise ProtocolViolation(f"{what}: per-seed 值含 NaN") + return vals + + +def bimodality_1d(values: Sequence[float]) -> Dict: + """1-D 二分聚类双峰检测（简化 dip：最优二分 + 间隙比）。 + + 返回 {"bimodal": bool, "split_at": float|None, "gap_ratio": float, + "groups": [[...], [...]]}。判定：类间间隙 / 类内散度 > 3.0 + **且**间隙 ≥ _MIN_BIMODAL_GAP（chance/满分两团中间是空带，间隙比 + 极大；量化分数的相邻取值则两条判据都过不了——见 _MIN_BIMODAL_GAP + 的注释）。宁可多报（bimodal=True 只是"不得引用"，不是"扔数据"）。 + """ + vals = sorted(float(v) for v in values) + if len(vals) < 4: + return _unimodal_result(vals) + best = _best_split(vals) + if best is None: # 全同值: 无间隙, 单峰 + return _unimodal_result(vals) + ratio, i = best + gap = vals[i] - vals[i - 1] + return {"bimodal": ratio > 3.0 and gap >= _MIN_BIMODAL_GAP, + "split_at": (vals[i - 1] + vals[i]) / 2, + "gap_ratio": round(ratio, 3), "groups": [vals[:i], vals[i:]]} + + +def forbid_bimodal(values: Sequence[float], what: str = "result") -> Dict: + b = bimodality_1d(values) + if b["bimodal"]: + raise ProtocolViolation( + f"{what}: per-seed 分布双峰 (gap_ratio={b['gap_ratio']}, " + f"split@{b['split_at']:.3f}) — 掷硬币区, 数据不得引用; " + "加 seeds/扩题集后重跑") + return b + + +def publishable(a: Sequence[float], b: Sequence[float], + min_seeds: int = 3, alpha: float = 0.05, + tag: str = "") -> Tuple[bool, Dict]: + """对照结果的"可引用"判定：≥min_seeds + 双臂非双峰 + 配对 p best[0]: + best = (ratio, i) + return best + + +def _as_floats(vals): + """数值化一臂；含 None/非数值/NaN 返回 None（脏臂已由 seeds 门槛记因， + 后续门槛跳过——publishable 的任何路径都不允许因脏数据崩溃）。""" + try: + nums = [float(v) for v in vals] + except (TypeError, ValueError): + return None + if any(math.isnan(x) for x in nums): + return None + return nums + + +def _gate_reasons(a, b, min_seeds, alpha, tag, report) -> List[str]: + """三道门槛（seeds / 双峰 / 配对检验）的未过原因收集。""" + reasons = [] + for name, vals in (("arm-a", a), ("arm-b", b)): + try: + require_min_seeds(vals, min_seeds, what=f"{tag} {name}") + except ProtocolViolation as e: + reasons.append(str(e)) + reasons += _bimodality_reasons(a, b, report) + reasons += _sign_test_reasons(a, b, alpha, report) + return reasons + + +def _bimodality_reasons(a, b, report) -> List[str]: + reasons = [] + for name, vals in (("arm-a", a), ("arm-b", b)): + nums = _as_floats(vals) + if nums is None or len(nums) < 4: + continue + bm = bimodality_1d(nums) + report[f"bimodality_{name}"] = bm + if bm["bimodal"]: + reasons.append(f"{name} 双峰 (gap_ratio={bm['gap_ratio']})") + return reasons + + +def _sign_test_reasons(a, b, alpha, report) -> List[str]: + if len(a) != len(b) or len(a) < 1: + return ["两臂 seeds 不齐, 无法配对"] + na, nb = _as_floats(a), _as_floats(b) + if na is None or nb is None: + return [] + st = paired_sign_test(na, nb) + report["sign_test"] = st + if st["p"] >= alpha: + return [f"配对符号检验 p={st['p']:.3f} ≥ {alpha}"] + return [] + + +def _unimodal_result(vals: List[float]) -> Dict: + return {"bimodal": False, "split_at": None, "gap_ratio": 0.0, + "groups": [vals, []]} + + +def _mean(xs): + return sum(xs) / len(xs) diff --git a/benchmarks/results/bench_adapter_fake_20260906_r1.log b/benchmarks/results/bench_adapter_fake_20260906_r1.log new file mode 100644 index 0000000..68510e7 --- /dev/null +++ b/benchmarks/results/bench_adapter_fake_20260906_r1.log @@ -0,0 +1,25 @@ +=== AwareLiquid adapter benchmark (lexical stand-in) === + + ingested annual-2023 -> 33 chunks + ingested bond-prospectus -> 32 chunks + ingested reg-filing -> 32 chunks + FTS5 lexical channel: available + + per-question recall@1 (dense -> hybrid): + q1: 1 -> 1 + q2: 1 -> 1 + q3: 0 -> 0 + q4: 0 -> 0 + q5: 1 -> 1 + q6: 1 -> 1 + q7: 1 -> 1 + q8: 1 -> 1 + +--- ablation: dense-only vs hybrid RRF --- + recall@1 (answer in TOP chunk): dense 6/8 (75%) -> hybrid 6/8 (75%) + recall@4 : dense 7/8 (88%) -> hybrid 8/8 (100%) + answer retention : dense 7/8 hybrid 8/8 + end-to-end valid rows : 8/8 + prompt context tokens : 1254 vs full-doc 22668 -> 94% fewer + +RESULT: PASS (hybrid recall@1 6/8 >= dense 6/8, recall@4 8/8 >= dense 7/8) diff --git a/benchmarks/results/bench_adapter_fake_20260906_r2.log b/benchmarks/results/bench_adapter_fake_20260906_r2.log new file mode 100644 index 0000000..68510e7 --- /dev/null +++ b/benchmarks/results/bench_adapter_fake_20260906_r2.log @@ -0,0 +1,25 @@ +=== AwareLiquid adapter benchmark (lexical stand-in) === + + ingested annual-2023 -> 33 chunks + ingested bond-prospectus -> 32 chunks + ingested reg-filing -> 32 chunks + FTS5 lexical channel: available + + per-question recall@1 (dense -> hybrid): + q1: 1 -> 1 + q2: 1 -> 1 + q3: 0 -> 0 + q4: 0 -> 0 + q5: 1 -> 1 + q6: 1 -> 1 + q7: 1 -> 1 + q8: 1 -> 1 + +--- ablation: dense-only vs hybrid RRF --- + recall@1 (answer in TOP chunk): dense 6/8 (75%) -> hybrid 6/8 (75%) + recall@4 : dense 7/8 (88%) -> hybrid 8/8 (100%) + answer retention : dense 7/8 hybrid 8/8 + end-to-end valid rows : 8/8 + prompt context tokens : 1254 vs full-doc 22668 -> 94% fewer + +RESULT: PASS (hybrid recall@1 6/8 >= dense 6/8, recall@4 8/8 >= dense 7/8) diff --git a/benchmarks/results/bench_adapter_fake_20260906_r3.log b/benchmarks/results/bench_adapter_fake_20260906_r3.log new file mode 100644 index 0000000..68510e7 --- /dev/null +++ b/benchmarks/results/bench_adapter_fake_20260906_r3.log @@ -0,0 +1,25 @@ +=== AwareLiquid adapter benchmark (lexical stand-in) === + + ingested annual-2023 -> 33 chunks + ingested bond-prospectus -> 32 chunks + ingested reg-filing -> 32 chunks + FTS5 lexical channel: available + + per-question recall@1 (dense -> hybrid): + q1: 1 -> 1 + q2: 1 -> 1 + q3: 0 -> 0 + q4: 0 -> 0 + q5: 1 -> 1 + q6: 1 -> 1 + q7: 1 -> 1 + q8: 1 -> 1 + +--- ablation: dense-only vs hybrid RRF --- + recall@1 (answer in TOP chunk): dense 6/8 (75%) -> hybrid 6/8 (75%) + recall@4 : dense 7/8 (88%) -> hybrid 8/8 (100%) + answer retention : dense 7/8 hybrid 8/8 + end-to-end valid rows : 8/8 + prompt context tokens : 1254 vs full-doc 22668 -> 94% fewer + +RESULT: PASS (hybrid recall@1 6/8 >= dense 6/8, recall@4 8/8 >= dense 7/8) diff --git a/benchmarks/results/pytest_20260907.log b/benchmarks/results/pytest_20260907.log new file mode 100644 index 0000000..835ba11 --- /dev/null +++ b/benchmarks/results/pytest_20260907.log @@ -0,0 +1,27 @@ +============================= test session starts ============================== +platform darwin -- Python 3.13.3, pytest-9.1.1, pluggy-1.6.0 +rootdir: /Users/aricredemption/Projects/AwareLiquid-M2 +configfile: pyproject.toml +testpaths: tests +plugins: anyio-4.14.2 +collected 197 items + +tests/test_afac_contract.py .................... [ 10%] +tests/test_agent.py ..................... [ 20%] +tests/test_bench_determinism.py .. [ 21%] +tests/test_check_results_refs.py .......... [ 26%] +tests/test_chunker.py ..... [ 29%] +tests/test_compressor.py ....... [ 32%] +tests/test_contracts_m0.py ......................... [ 45%] +tests/test_evidence.py ............................ [ 59%] +tests/test_evidence_index.py ..... [ 62%] +tests/test_experiment_protocol.py ......... [ 67%] +tests/test_formal_reasoning.py ...... [ 70%] +tests/test_hybrid.py ........ [ 74%] +tests/test_lexical_store.py ....... [ 77%] +tests/test_multi_query.py ...... [ 80%] +tests/test_qwen_client.py ......................... [ 93%] +tests/test_schemas.py ......... [ 97%] +tests/test_submit.py .... [100%] + +============================= 197 passed in 6.04s ============================== diff --git a/data/questions/group_a/financial_contracts_questions.json b/data/questions/group_a/financial_contracts_questions.json new file mode 100644 index 0000000..de66612 --- /dev/null +++ b/data/questions/group_a/financial_contracts_questions.json @@ -0,0 +1,352 @@ +[ + { + "qid": "fc_a_001", + "domain": "financial_contracts", + "split": "A", + "question": "基于提供的两份债券募集说明书摘要，关于发行主体、发行规模及中介机构安排，下列哪些描述是准确的？", + "options": { + "A": "第一份文档的发行人名称为广东省广晟控股集团有限公司", + "B": "第二份文档的发行金额上限低于第一份文档", + "C": "两份文档的主体信用评级均为 AAA", + "D": "第二份文档明确指定国信证券股份有限公司为受托管理人" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "text01", + "text02" + ] + }, + { + "qid": "fc_a_002", + "domain": "financial_contracts", + "split": "A", + "question": "对比两份文档中的债券发行要素与评级信息，以下哪些结论成立？", + "options": { + "A": "两份文档的发行人主体信用评级均达到 AAA 级别", + "B": "第二份文档的本期发行金额上限高于第一份文档", + "C": "两份文档均明确标注了债项信用评级为负值", + "D": "第二份文档的发行人属于证券公司类别" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "text01", + "text14" + ] + }, + { + "qid": "fc_a_003", + "domain": "financial_contracts", + "split": "A", + "question": "两份文档均包含关于发行人将及时、公平地履行信息披露义务的明确承诺条款。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "text01", + "text03" + ] + }, + { + "qid": "fc_a_004", + "domain": "financial_contracts", + "split": "A", + "question": "结合两份文档内容，关于发行限额与财务指标披露情况，下列说法正确的是？", + "options": { + "A": "第一份文档显示的本期债券发行金额上限为 10 亿元", + "B": "第二份文档中提及的注册金额上限为 180 亿元", + "C": "两份文档均详细列出了发行人最近三年的净利润数据", + "D": "第二份文档中提供了具体的资产负债率历史数据，数值在 63% 至 66% 之间" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "text01", + "text13" + ] + }, + { + "qid": "fc_a_005", + "domain": "financial_contracts", + "split": "A", + "question": "针对两份文档所涉及的发行主体及交易结构特征，以下哪些判断符合文档内容？", + "options": { + "A": "第一份文档的发行人是广东省广晟控股集团有限公司", + "B": "第二份文档的内容涉及发行股份购买资产并募集配套资金的交易结构", + "C": "两份文档均明确给出了发行人的合并口径资产负债率具体数值", + "D": "第二份文档中标的公司控股股东的资产负债率为 43.24%" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "text01", + "text10" + ] + }, + { + "qid": "fc_a_006", + "domain": "financial_contracts", + "split": "A", + "question": "文档 fc_text_001 和 fc_text_004 均提及了涉及债券持有人权利的保护性条款，如回售选择权或相关赎回安排。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "text01", + "text04" + ] + }, + { + "qid": "fc_a_007", + "domain": "financial_contracts", + "split": "A", + "question": "关于文档 fc_text_002 和 fc_text_014 的内容，下列哪些说法是正确的？", + "options": { + "A": "两家发行人的主体信用评级均为 AAA 级", + "B": "两份文件均对本期债券的发行规模设定了上限", + "C": "两家发行人均为证券公司", + "D": "两份文件均提到了具体的违约情形描述或相关条款" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "text02", + "text14" + ] + }, + { + "qid": "fc_a_008", + "domain": "financial_contracts", + "split": "A", + "question": "结合文档 fc_text_002 和 fc_text_003 的信息，以下哪项描述准确反映了其中关于违约赔偿或发行规模的细节？", + "options": { + "A": "文档 fc_text_002 显示本期债券发行金额为 10 亿元", + "B": "文档 fc_text_003 中提到的违约赔偿计算公式包含 150% 的惩罚系数", + "C": "文档 fc_text_002 的主体信用评级为 AAA 级", + "D": "文档 fc_text_003 明确记载发行人是厦门金圆投资集团有限公司且无任何其他信息冲突" + }, + "answer_format": "mcq", + "type": "单选题", + "doc_ids": [ + "text02", + "text03" + ] + }, + { + "qid": "fc_a_009", + "domain": "financial_contracts", + "split": "A", + "question": "对比文档 fc_text_002 和 fc_text_010 的内容，下列哪些陈述符合文档披露的事实？", + "options": { + "A": "文档 fc_text_002 的发行人主体评级为 AA+", + "B": "文档 fc_text_010 披露的标的公司控股股东力诺投资在 2024 年 12 月 31 日的资产负债率为 43.24%", + "C": "文档 fc_text_002 的发行金额上限为 10 亿元", + "D": "两份文档均提及了关于违约或补偿的相关条款" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "text02", + "text10" + ] + }, + { + "qid": "fc_a_010", + "domain": "financial_contracts", + "split": "A", + "question": "基于给定文档，判断陈述是否正确：文档 fc_text_006 中明确列出了转股价格向下修正条款，而文档 fc_text_002 中未出现该特定术语。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "text02", + "text06" + ] + }, + { + "qid": "fc_a_011", + "domain": "financial_contracts", + "split": "A", + "question": "关于文档中涉及的发行主体信息及文件基本要素，下列描述正确的有？", + "options": { + "A": "fc_text_002 的发行人名称中包含“深圳市融资租赁”字样", + "B": "fc_text_007 中明确标注了证券上市地点为深圳证券交易所", + "C": "fc_text_002 的债券发行金额上限设定为 10 亿元", + "D": "fc_text_007 的文件类型被定义为面向专业投资者的债券募集说明书" + }, + "answer_format": "multi", + "type": "事实核对与属性比对", + "doc_ids": [ + "text02", + "text07" + ] + }, + { + "qid": "fc_a_012", + "domain": "financial_contracts", + "split": "A", + "question": "结合两份文档内容，关于发行人责任承诺及财务指标的描述，下列选项正确的有？", + "options": { + "A": "fc_text_003 中明确约定了回售或赎回部分债券的兑付日为 2031 年 4 月 23 日", + "B": "fc_text_013 中披露的利润总额呈逐年下降趋势", + "C": "fc_text_013 中发行人资产负债率从 66.38% 降至 63.51%，总体呈下降趋势", + "D": "fc_text_003 中违约利息计算公式包含 150% 的系数" + }, + "answer_format": "multi", + "type": "数据趋势与条款细节比对", + "doc_ids": [ + "text03", + "text13" + ] + }, + { + "qid": "fc_a_013", + "domain": "financial_contracts", + "split": "A", + "question": "根据提供的文档片段，判断以下陈述是否正确：两份文档均提及了董事及高级管理人员需对文件内容的真实性承担责任，且其中一份文档披露了标的公司控股股东力诺投资的资产负债率为 43.24%。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "一致性校验", + "doc_ids": [ + "text03", + "text10" + ] + }, + { + "qid": "fc_a_014", + "domain": "financial_contracts", + "split": "A", + "question": "对比两份文档中的违约责任与特殊条款，以下说法正确的有？", + "options": { + "A": "fc_text_003 约定的违约利息计算基数包含本金和利息", + "B": "fc_text_008 规定若触发资产减值补偿条款，甲方应在报告出具之日起 10 日内通知", + "C": "fc_text_003 中债券品种一的兑付日设定在 2031 年", + "D": "fc_text_008 中资产减值补偿通知期限被设定为 5 日" + }, + "answer_format": "multi", + "type": "违约责任与条款细节比对", + "doc_ids": [ + "text03", + "text08" + ] + }, + { + "qid": "fc_a_015", + "domain": "financial_contracts", + "split": "A", + "question": "基于文档信息，关于两家发行人的债券发行要素，下列哪一项描述是准确的？", + "options": { + "A": "fc_text_003 的发行人主体评级为 AAA 级", + "B": "fc_text_014 的注册金额为不超过 180 亿元", + "C": "fc_text_003 的违约罚息利率倍数为 200%", + "D": "fc_text_014 的主承销商为国信证券股份有限公司" + }, + "answer_format": "mcq", + "type": "发行要素识别", + "doc_ids": [ + "text03", + "text14" + ] + }, + { + "qid": "fc_a_016", + "domain": "financial_contracts", + "split": "A", + "question": "关于所涉可转换公司债券的发行条款与财务影响，以下哪些描述符合文档记载？", + "options": { + "A": "其中一份文档明确记载了初始转股价为每股 19.59 元", + "B": "两份文档均提及本次发行的可转债包含有条件赎回条款", + "C": "其中一份文档指出全部转股后公司资产负债率将下降至 26.99%", + "D": "所有文档显示的保荐机构均为中国国际金融股份有限公司" + }, + "answer_format": "multi", + "type": "clause_verification", + "doc_ids": [ + "text04", + "text09" + ] + }, + { + "qid": "fc_a_017", + "domain": "financial_contracts", + "split": "A", + "question": "结合相关募集说明书内容，关于可转换债券的基本条款与发行信息，下列说法正确的是？", + "options": { + "A": "两份文档均涉及转股价格向下修正的相关条款", + "B": "第一份文档的股票代码与第二份文档的股票代码不一致", + "C": "两份文档均明确标注发行日期为 2025 年 9 月", + "D": "两份文档均提到了可转债设置有条件赎回条款" + }, + "answer_format": "multi", + "type": "clause_verification", + "doc_ids": [ + "text04", + "text05" + ] + }, + { + "qid": "fc_a_018", + "domain": "financial_contracts", + "split": "A", + "question": "根据文档披露的时间信息，第二份文件中的可转债发行公告日期晚于第一份文件中的发行日期。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "time_comparison", + "doc_ids": [ + "text04", + "text06" + ] + }, + { + "qid": "fc_a_019", + "domain": "financial_contracts", + "split": "A", + "question": "对比两份文档中的证券信息与财务数据披露情况，下列判断成立的是？", + "options": { + "A": "第一份文档中标注的股票代码与第二份文档中标注的代码不同", + "B": "第二份文档中列示了具体的资产负债率数值，如 5.70% 和 35.83%", + "C": "第一份文档详细披露了转股后的具体资产负债率预测值", + "D": "第二份文档的证券简称是安克创新" + }, + "answer_format": "multi", + "type": "data_consistency_check", + "doc_ids": [ + "text04", + "text07" + ] + }, + { + "qid": "fc_a_020", + "domain": "financial_contracts", + "split": "A", + "question": "依据文档内容，关于可转换债券的初始转股价格及发行人信息，正确的陈述有？", + "options": { + "A": "第二份文档披露的初始转股价格为 18.26 元/股", + "B": "第一份文档显示的发行人股票简称是安克创新", + "C": "第二份文档显示的发行人股票代码为 300866", + "D": "第二份文档声明初始转股价格不低于募集说明书公告之日的市场价格" + }, + "answer_format": "multi", + "type": "clause_verification", + "doc_ids": [ + "text04", + "text11" + ] + } +] \ No newline at end of file diff --git a/data/questions/group_a/financial_reports_questions.json b/data/questions/group_a/financial_reports_questions.json new file mode 100644 index 0000000..847704e --- /dev/null +++ b/data/questions/group_a/financial_reports_questions.json @@ -0,0 +1,352 @@ +[ + { + "qid": "fin_a_001", + "domain": "financial_reports", + "split": "A", + "question": "根据比亚迪连续两年的年度报告，下列关于公司经营业绩变化的描述中，哪些是准确的？", + "options": { + "A": "2025 年营业收入较 2024 年实现增长", + "B": "2025 年归属于上市公司股东的净利润出现下滑", + "C": "2025 年经营活动产生的现金流量净额优于 2024 年", + "D": "2025 年研发投入占营业收入的比例较 2024 年有所下降" + }, + "answer_format": "multi", + "type": "财务指标对比分析", + "doc_ids": [ + "annual_byd_2024_report", + "annual_byd_2025_report" + ] + }, + { + "qid": "fin_a_002", + "domain": "financial_reports", + "split": "A", + "question": "结合宁德时代 2024 年及 2025 年年度报告，关于其财务表现与股东回报政策的陈述，哪些符合事实？", + "options": { + "A": "2025 年营业收入相比 2024 年实现了正增长", + "B": "2025 年拟派发现金分红金额高于 2024 年方案", + "C": "2025 年经营活动产生的现金流量净额较上年减少", + "D": "2025 年实施了按净利润 50% 比例的现金分红政策" + }, + "answer_format": "multi", + "type": "财务指标对比分析", + "doc_ids": [ + "annual_catl_2024_report", + "annual_catl_2025_report" + ] + }, + { + "qid": "fin_a_003", + "domain": "financial_reports", + "split": "A", + "question": "依据中国建筑 2024 年与 2025 年年度报告披露的信息，判断以下说法是否正确：2025 年公司归属于上市公司股东的净利润同比出现下降，且现金分红占合并报表归属于上市公司股东净利润的比例有所提升。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "财务数据一致性校验", + "doc_ids": [ + "annual_cscec_2024_report", + "annual_cscec_2025_report" + ] + }, + { + "qid": "fin_a_004", + "domain": "financial_reports", + "split": "A", + "question": "对照美的集团 2024 年与 2025 年年度报告，关于公司业绩增长与资金运作情况的描述，哪些是正确的？", + "options": { + "A": "2025 年营业总收入的增长率高于 2024 年的增长率", + "B": "2025 年经营活动产生的现金流量净额低于 2024 年同期水平", + "C": "2025 年研发投入占营业收入的比例较 2024 年有明显提升", + "D": "2025 年度公司现金分红与股份回购之总金额超过了当年归母净利润" + }, + "answer_format": "multi", + "type": "财务指标对比分析", + "doc_ids": [ + "annual_midea_2024_report", + "annual_midea_2025_report" + ] + }, + { + "qid": "fin_a_005", + "domain": "financial_reports", + "split": "A", + "question": "基于比亚迪与美的集团 2024 年年度报告的数据对比，关于两家企业关键财务指标的结论，哪些是成立的？", + "options": { + "A": "比亚迪 2024 年的营业收入规模大于美的集团", + "B": "比亚迪 2024 年的研发投入强度高于美的集团", + "C": "美的集团 2024 年的经营活动现金流净额高于比亚迪", + "D": "两家公司 2024 年归属于上市公司股东的净利润均实现了双位数同比增长" + }, + "answer_format": "multi", + "type": "跨公司财务指标对比", + "doc_ids": [ + "annual_byd_2024_report", + "annual_midea_2024_report" + ] + }, + { + "qid": "fin_a_006", + "domain": "financial_reports", + "split": "A", + "question": "根据提供的年度报告数据，比亚迪 2024 年经营活动产生的现金流量净额低于其营业收入的一半，而美的集团 2025 年经营活动产生的现金流量净额则超过了其营业收入的十分之一。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "annual_byd_2024_report", + "annual_midea_2025_report" + ] + }, + { + "qid": "fin_a_007", + "domain": "financial_reports", + "split": "A", + "question": "关于比亚迪 2024 年和宁德时代 2025 年的财务表现，以下哪些描述是准确的？", + "options": { + "A": "比亚迪 2024 年研发投入占营业收入比例为 6.97%", + "B": "宁德时代 2025 年度现金分红方案为每 10 股派发现金分红 69.57 元（含税）", + "C": "比亚迪 2024 年经营活动产生的现金流量净额较上年同期有所增长", + "D": "宁德时代 2025 年经营活动产生的现金流量净额同比增长超过 30%" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "annual_byd_2024_report", + "annual_catl_2025_report" + ] + }, + { + "qid": "fin_a_008", + "domain": "financial_reports", + "split": "A", + "question": "结合两份年报信息，关于利润分配及研发投入的描述，下列哪一项是正确的？", + "options": { + "A": "比亚迪 2024 年研发费用占营业收入的比例为 6.63%", + "B": "宁德时代 2024 年度利润分配预案中，现金分红金额占当年归母净利润的比例为 20%", + "C": "比亚迪 2024 年归属于上市公司股东的净利润为 300.4 亿元", + "D": "宁德时代 2024 年每股派发现金分红 45.53 元" + }, + "answer_format": "mcq", + "type": "单选题", + "doc_ids": [ + "annual_byd_2024_report", + "annual_catl_2024_report" + ] + }, + { + "qid": "fin_a_009", + "domain": "financial_reports", + "split": "A", + "question": "对比比亚迪 2024 年与中国移动 2025 年的相关财务指标，以下说法正确的有哪些？", + "options": { + "A": "中国移动 2025 年营业收入超过 1 万亿元人民币", + "B": "比亚迪 2024 年研发投入占营业收入比例高于中国移动 2025 年的相应比例", + "C": "两家公司的经营活动产生的现金流量净额均为正数", + "D": "中国移动 2025 年归属于母公司股东的净利润实现双位数增长" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "annual_byd_2024_report", + "annual_chinamobile_2025_report" + ] + }, + { + "qid": "fin_a_010", + "domain": "financial_reports", + "split": "A", + "question": "在提供的文档中，比亚迪 2025 年的研发投入占营业收入比例相较于 2024 年有所上升，且美的集团自 2019 年起连续实施了股份回购方案。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "annual_byd_2025_report", + "annual_midea_2024_report" + ] + }, + { + "qid": "fin_a_011", + "domain": "financial_reports", + "split": "A", + "question": "关于比亚迪与美的集团 2025 年年度报告披露的经营成果及分红安排，下列陈述正确的有？", + "options": { + "A": "美的集团归母净利润增速快于比亚迪", + "B": "比亚迪研发投入占比低于美的集团", + "C": "美的集团每股现金分红总额高于比亚迪", + "D": "双方 2025 年经营活动现金流净额均为正值" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "annual_byd_2025_report", + "annual_midea_2025_report" + ] + }, + { + "qid": "fin_a_012", + "domain": "financial_reports", + "split": "A", + "question": "对比比亚迪与中国移动 2025 年财务数据，以下结论成立的是？", + "options": { + "A": "中国移动营收规模大于比亚迪", + "B": "比亚迪净利润降幅超过一成半", + "C": "中国移动研发费用占比高于比亚迪", + "D": "两家公司经营活动现金流净额同比均呈下降趋势" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "annual_byd_2025_report", + "annual_chinamobile_2025_report" + ] + }, + { + "qid": "fin_a_013", + "domain": "financial_reports", + "split": "A", + "question": "判断：宁德时代 2024 年度拟派发的每股现金分红金额高于比亚迪 2025 年度建议派发的末期股息金额。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "annual_byd_2025_report", + "annual_catl_2024_report" + ] + }, + { + "qid": "fin_a_014", + "domain": "financial_reports", + "split": "A", + "question": "基于比亚迪与宁德时代 2025 年年报信息，关于业绩增长与分红政策的描述，正确的选项是？", + "options": { + "A": "宁德时代营业收入增速高于比亚迪", + "B": "宁德时代 2025 年现金分红占净利润比例约为 50%", + "C": "比亚迪研发费用占营收比例不足 5%", + "D": "两家公司 2025 年经营活动现金流净额均实现正增长" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "annual_byd_2025_report", + "annual_catl_2025_report" + ] + }, + { + "qid": "fin_a_015", + "domain": "financial_reports", + "split": "A", + "question": "依据宁德时代 2024 年与中国移动 2025 年年报，关于利润分配与营收情况的单一正确描述是？", + "options": { + "A": "宁德时代 2024 年利润分配预案中现金分红占归母净利润比例为 20%", + "B": "中国移动 2025 年营业收入同比减少了 0.9%", + "C": "宁德时代 2024 年每股分红金额为 69.57 元", + "D": "中国移动 2025 年研发费用占营业收入比重超过 5%" + }, + "answer_format": "mcq", + "type": "单选题", + "doc_ids": [ + "annual_catl_2024_report", + "annual_chinamobile_2025_report" + ] + }, + { + "qid": "fin_a_016", + "domain": "financial_reports", + "split": "A", + "question": "关于宁德时代 2024 年年度报告与美的集团 2025 年年度报告中披露的利润分配方案，以下说法正确的有？", + "options": { + "A": "宁德时代 2024 年拟每 10 股派发现金红利 45.53 元（含税）", + "B": "美的集团 2025 年拟每 10 股派发现金红利 38 元（含税）", + "C": "两家公司在对应年份均实施了资本公积金转增股本", + "D": "美的集团 2025 年现金分红与股份回购总金额超过当年归母净利润" + }, + "answer_format": "multi", + "type": "财务分配政策比较", + "doc_ids": [ + "annual_catl_2024_report", + "annual_midea_2025_report" + ] + }, + { + "qid": "fin_a_017", + "domain": "financial_reports", + "split": "A", + "question": "对比宁德时代与中国建筑 2024 年年度报告中的财务及分配情况，下列描述准确的有？", + "options": { + "A": "宁德时代 2024 年筹资活动产生的现金流量净额较上年减少约 292 亿元", + "B": "中国建筑 2024 年新签合同额达到 2.19 万亿元", + "C": "中国建筑 2024 年现金分红占归母净利润比例约为 24.29%", + "D": "宁德时代 2024 年利润分配预案中包含了送红股方案" + }, + "answer_format": "multi", + "type": "财务现金流与分红比例核对", + "doc_ids": [ + "annual_catl_2024_report", + "annual_cscec_2024_report" + ] + }, + { + "qid": "fin_a_018", + "domain": "financial_reports", + "split": "A", + "question": "判断题：根据 2025 年年度报告，宁德时代与美的集团的营业收入均实现双位数增长，且宁德时代的净利润增速高于美的集团。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "业绩增长趋势判断", + "doc_ids": [ + "annual_catl_2025_report", + "annual_midea_2025_report" + ] + }, + { + "qid": "fin_a_019", + "domain": "financial_reports", + "split": "A", + "question": "关于宁德时代 2025 年及美的集团 2024 年的股东回报措施，以下说法正确的有？", + "options": { + "A": "宁德时代 2025 年以净利润的 50% 实施现金分红", + "B": "美的集团自 2019 年起连续四年推出股份回购方案", + "C": "宁德时代 2025 年每 10 股派发现金分红 38 元", + "D": "美的集团 2024 年营业总收入超过 4500 亿元" + }, + "answer_format": "multi", + "type": "股东回报措施对比", + "doc_ids": [ + "annual_catl_2025_report", + "annual_midea_2024_report" + ] + }, + { + "qid": "fin_a_020", + "domain": "financial_reports", + "split": "A", + "question": "基于宁德时代与中国移动 2025 年年度报告数据，关于企业规模与研发投入的描述，正确的选项是？", + "options": { + "A": "中国移动 2025 年营业收入超过宁德时代同期营收的两倍", + "B": "宁德时代 2025 年经营活动产生的现金流量净额为 1332 亿元", + "C": "中国移动 2025 年研发费用占营业收入比重为 3.9%", + "D": "宁德时代 2025 年境外收入占比超过 50%" + }, + "answer_format": "multi", + "type": "企业规模与研发投入分析", + "doc_ids": [ + "annual_catl_2025_report", + "annual_chinamobile_2025_report" + ] + } +] \ No newline at end of file diff --git a/data/questions/group_a/insurance_questions.json b/data/questions/group_a/insurance_questions.json new file mode 100644 index 0000000..7e53c4a --- /dev/null +++ b/data/questions/group_a/insurance_questions.json @@ -0,0 +1,394 @@ +[ + { + "qid": "ins_a_001", + "domain": "insurance", + "split": "A", + "question": "关于四个养老保险/寿险产品的“身故保险金”，以下计算结果正确的是？假设所有产品的已交保费均为100万元，现金价值均为80万元。平安智盈金生（领取日前）保单账户价值90万元；国寿增益宝（一人，40岁）基本保额90万元，个人账户价值85万元；国寿鑫享添盈已领养老年金20万元；平安富鸿金生已领养老年金15万元。则各自身故保险金金额排序正确的是？", + "options": { + "A": "平安智盈金生(90万) > 国寿增益宝(144万) > 国寿鑫享添盈(80万) > 平安富鸿金生(85万)", + "B": "国寿增益宝(144万) > 平安智盈金生(90万) > 平安富鸿金生(85万) > 国寿鑫享添盈(80万)", + "C": "国寿增益宝(144万) > 平安智盈金生(90万) > 国寿鑫享添盈(85万) > 平安富鸿金生(80万)", + "D": "平安智盈金生(90万) = 国寿增益宝(90万) > 国寿鑫享添盈(80万) = 平安富鸿金生(80万)" + }, + "answer_format": "mcq", + "type": "推理判断", + "doc_ids": [ + "1", + "2", + "15", + "16" + ] + }, + { + "qid": "ins_a_002", + "domain": "insurance", + "split": "A", + "question": "假设投保人分别投保了平安智盈金生、国寿增益宝、平安富鸿金生。平安智盈金生累计所交保费10万元，保单账户累计收益2万元，在第7个保单年度末退保；国寿增益宝个人账户价值12万元，在第7个保单年度末退保（退保费用0%）；平安富鸿金生现金价值9万元。则三者退保所得金额从高到低排序为？", + "options": { + "A": "国寿增益宝(12万) > 平安智盈金生(11.5万) > 平安富鸿金生(9万)", + "B": "平安智盈金生(11.5万) > 国寿增益宝(12万) > 平安富鸿金生(9万)", + "C": "国寿增益宝(11.76万) > 平安智盈金生(11.5万) > 平安富鸿金生(9万)", + "D": "平安智盈金生(12万) > 国寿增益宝(11.76万) > 平安富鸿金生(9万)" + }, + "answer_format": "mcq", + "type": "计算题", + "doc_ids": [ + "1", + "2", + "16" + ] + }, + { + "qid": "ins_a_003", + "domain": "insurance", + "split": "A", + "question": "李某投保了众安白血病医疗险、平安e生保（计划二，免赔额5000元）、太保团体百万医疗（免赔额1万元）。李某因白血病复发住院，总费用8万元，医保报销3万元，自费5万元。假设所有费用均属保险责任，众安白血病医疗险免赔额为0，且为形态学复发。则三家保险公司共应赔付多少？", + "options": { + "A": "众安：赔付5万；平安e生保：4.5万；太保：4万；合计13.5万", + "B": "众安：赔付5万；平安e生保：0万；太保：0万；合计5万", + "C": "众安：赔付2.5万；平安e生保：1.25万；太保：1.25万；合计5万", + "D": "无法确定，因众安免赔额和复发类型未知" + }, + "answer_format": "mcq", + "type": "计算题", + "doc_ids": [ + "3", + "5", + "6" + ] + }, + { + "qid": "ins_a_004", + "domain": "insurance", + "split": "A", + "question": "关于“等待期”内出险的处理，以下哪种情形下保险公司需要承担赔偿责任？", + "options": { + "A": "投保平安安佑福重疾险第50天，因意外车祸导致双目失明（符合重大疾病）", + "B": "投保众安白血病医疗险第20天，确诊白血病（非意外）", + "C": "投保平安e生保第15天，因流感住院（非意外）", + "D": "投保太保团体百万医疗第10天，因急性阑尾炎手术（非意外）" + }, + "answer_format": "mcq", + "type": "逻辑推理", + "doc_ids": [ + "4", + "5", + "6", + "12" + ] + }, + { + "qid": "ins_a_005", + "domain": "insurance", + "split": "A", + "question": "下列哪些情形属于相应保险产品的免责范围？", + "options": { + "A": "平安智盈金生：被保险人因酒后驾驶导致失能失智护理状态", + "B": "平安安佑福重疾险：被保险人因感染艾滋病病毒（非输血、职业、器官移植）导致重大疾病", + "C": "平安预防接种意外险：被保险人在不具有接种条件的单位接种疫苗后发生异常反应", + "D": "众安食责险：食品超过保质期导致消费者食物中毒" + }, + "answer_format": "multi", + "type": "逻辑推理", + "doc_ids": [ + "1", + "4", + "7", + "13" + ] + }, + { + "qid": "ins_a_006", + "domain": "insurance", + "split": "A", + "question": "王某投保了平安e生保（计划一：免赔额1万元，家庭共享）和太保团体百万医疗（免赔额1万元）。王某家庭三人同时参保e生保计划一，共享免赔额。某年度，王某本人发生医疗费用2万元（全部属保险责任），医保报销8000元；其配偶发生医疗费用1.5万元，医保报销6000元。则e生保和太保分别应赔付多少？假设王某家庭未从其他途径获得补偿，两份医疗保险分别按各自条款公式计算，不考虑彼此之间已获赔金额的扣除。", + "options": { + "A": "e生保赔付1.1万元，太保赔付0.2万元，合计1.3万元", + "B": "e生保赔付0.2万元，太保赔付0.9万元，合计1.1万元", + "C": "e生保赔付1.1万元，太保赔付0万元，合计1.1万元", + "D": "e生保赔付0.2万元，太保赔付0万元，合计0.2万元" + }, + "answer_format": "mcq", + "type": "计算题", + "doc_ids": [ + "5", + "6" + ] + }, + { + "qid": "ins_a_007", + "domain": "insurance", + "split": "A", + "question": "关于“保单贷款”，以下哪些说法正确？", + "options": { + "A": "平安智盈金生允许保单贷款，最高为现金价值的80%", + "B": "国寿增益宝允许保单贷款，最高为现金价值扣除欠款后的80%", + "C": "平安富鸿金生若按个人养老金制度投保，则不允许保单贷款", + "D": "平安富鸿金生无论何种投保方式，均不允许保单贷款" + }, + "answer_format": "multi", + "type": "推理判断", + "doc_ids": [ + "1", + "2", + "16" + ] + }, + { + "qid": "ins_a_008", + "domain": "insurance", + "split": "A", + "question": "关于“特定药品费用”的报销，下列哪些说法符合条款规定？", + "options": { + "A": "众安白血病医疗险要求特定药品须在保险人指定药店购买，且需处方审核", + "B": "平安安佑福重疾险不涵盖院外特定药品费用", + "C": "平安e生保的院外恶性肿瘤特定药品费用需经处方审核，且与指定药店直接结算", + "D": "太保团体百万医疗涵盖所有院外药品费用" + }, + "answer_format": "multi", + "type": "比较分析", + "doc_ids": [ + "3", + "4", + "5", + "6" + ] + }, + { + "qid": "ins_a_009", + "domain": "insurance", + "split": "A", + "question": "张某在驾驶特种车作业时，因操作不当导致车辆侧翻，本人受伤住院。他投保了平安预防接种意外险、众安营运交通意外险、平安特种车险（含车上人员责任险）。以下哪些产品可以赔付张某的医疗费用？", + "options": { + "A": "平安预防接种意外险（与预防接种无关）", + "B": "众安营运交通意外险（该险种保障乘坐营运交通工具，特种车不属于营运交通工具）", + "C": "平安特种车险的车上人员责任险", + "D": "众安特种车险的车上人员责任险" + }, + "answer_format": "multi", + "type": "逻辑推理", + "doc_ids": [ + "7", + "8", + "9" + ] + }, + { + "qid": "ins_a_010", + "domain": "insurance", + "split": "A", + "question": "关于“现金价值”的表述，下列哪些产品的条款中明确给出了具体的计算方法或公式？", + "options": { + "A": "平安智盈金生（给出了不同保单年度的比例公式）", + "B": "国寿增益宝（现金价值=个人账户价值-退保费用）", + "C": "平安安佑福重疾险（保单年度末现金价值载明，未给公式）", + "D": "平安富鸿金生（保单年度末现金价值载明，未给公式）" + }, + "answer_format": "multi", + "type": "事实查询", + "doc_ids": [ + "1", + "2", + "4", + "16" + ] + }, + { + "qid": "ins_a_011", + "domain": "insurance", + "split": "A", + "question": "李某同时投保了众安白血病医疗险、平安e生保、太保团体百万医疗、众安营运交通意外险。某日李某因车祸导致骨折住院，总费用5万元，医保报销2万元。以下哪些产品可以赔付？", + "options": { + "A": "众安白血病医疗险（仅保障白血病复发，不赔骨折）", + "B": "平安e生保（一般医疗保险金可赔付）", + "C": "太保团体百万医疗（一般医疗保险金可赔付）", + "D": "众安营运交通意外险（意外医疗？该产品仅保障身故和伤残，无医疗费用责任）" + }, + "answer_format": "multi", + "type": "推理判断", + "doc_ids": [ + "3", + "5", + "6", + "8" + ] + }, + { + "qid": "ins_a_012", + "domain": "insurance", + "split": "A", + "question": "关于“宽限期”后合同效力中止的处理，下列哪些产品的条款中明确：效力中止期间发生保险事故，保险人不承担保险责任？", + "options": { + "A": "国寿增益宝", + "B": "太保团体百万医疗", + "C": "平安家财险", + "D": "平安食品安全责任险" + }, + "answer_format": "multi", + "type": "事实查询", + "doc_ids": [ + "2", + "6", + "11", + "14" + ] + }, + { + "qid": "ins_a_013", + "domain": "insurance", + "split": "A", + "question": "三个养老保险产品（平安智盈金生、国寿鑫享添盈、平安富鸿金生）关于“养老年金开始领取日”的变更规定，以下哪项正确？", + "options": { + "A": "平安智盈金生允许在开始领取日前变更领取年龄和方式；国寿鑫享添盈不允许变更；平安富鸿金生允许变更领取年龄、方式、期间", + "B": "三者均不允许变更", + "C": "三者均允许在开始领取日前任意变更", + "D": "仅平安富鸿金生允许变更" + }, + "answer_format": "mcq", + "type": "比较分析", + "doc_ids": [ + "1", + "15", + "16" + ] + }, + { + "qid": "ins_a_014", + "domain": "insurance", + "split": "A", + "question": "赵某因意外事故导致双耳失聪，他投保了以下哪些产品可以获得赔付？", + "options": { + "A": "平安安佑福重疾险（双耳失聪属于重大疾病）", + "B": "平安e生保（住院医疗可赔付相关治疗费用）", + "C": "平安预防接种意外险（与预防接种无关，不赔）", + "D": "众安营运交通意外险（意外伤残，按伤残等级赔付）" + }, + "answer_format": "multi", + "type": "逻辑推理", + "doc_ids": [ + "4", + "5", + "7", + "8" + ] + }, + { + "qid": "ins_a_015", + "domain": "insurance", + "split": "A", + "question": "关于“故意自伤或自杀”的免责，以下哪些产品的条款中明确将“自杀”列为责任免除，但规定了2年后的例外？", + "options": { + "A": "平安智盈金生（只提到身故责任中的自杀2年内免责）", + "B": "平安安佑福重疾险（重大疾病中故意自伤免责，身故中自杀2年内免责）", + "C": "平安预防接种意外险（自杀免责，但未提2年例外）", + "D": "众安食责险（不涉及自杀）" + }, + "answer_format": "multi", + "type": "比较分析", + "doc_ids": [ + "1", + "4", + "7", + "13" + ] + }, + { + "qid": "ins_a_016", + "domain": "insurance", + "split": "A", + "question": "关于“施救费用”的赔偿上限，下列哪些产品的条款中明确规定最高不超过保险金额？", + "options": { + "A": "平安e生保", + "B": "太保团体百万医疗", + "C": "平安特种车险", + "D": "众安特种车险" + }, + "answer_format": "multi", + "type": "事实查询", + "doc_ids": [ + "5", + "6", + "9", + "10" + ] + }, + { + "qid": "ins_a_017", + "domain": "insurance", + "split": "A", + "question": "某被保险人同时拥有国寿增益宝、众安白血病医疗险、众安营运交通意外险、平安家财险。以下哪些事故会导致相应产品的赔付？", + "options": { + "A": "国寿增益宝：被保险人身故", + "B": "众安白血病医疗险：白血病复发住院", + "C": "众安营运交通意外险：乘坐公交车发生车祸导致伤残", + "D": "平安家财险：家中因火灾导致财产损失" + }, + "answer_format": "multi", + "type": "逻辑推理", + "doc_ids": [ + "2", + "3", + "8", + "11" + ] + }, + { + "qid": "ins_a_018", + "domain": "insurance", + "split": "A", + "question": "关于“免赔额”的抵扣，以下哪些说法正确？", + "options": { + "A": "众安白血病医疗险的免赔额可由基本医疗保险统筹账户补偿抵扣", + "B": "平安e生保的免赔额可由基本医疗保险个人账户支出抵扣", + "C": "太保团体百万医疗的免赔额可由从其他商业保险获得的补偿抵扣", + "D": "平安安佑福重疾险无免赔额" + }, + "answer_format": "multi", + "type": "比较分析", + "doc_ids": [ + "3", + "4", + "5", + "6" + ] + }, + { + "qid": "ins_a_019", + "domain": "insurance", + "split": "A", + "question": "假设四款产品（平安智盈金生、国寿增益宝、平安安佑福、平安富鸿金生）的投保人均在犹豫期内解除合同，则以下哪些产品会退还全部已交保险费？", + "options": { + "A": "平安智盈金生", + "B": "国寿增益宝", + "C": "平安安佑福", + "D": "平安富鸿金生" + }, + "answer_format": "multi", + "type": "事实查询", + "doc_ids": [ + "1", + "2", + "4", + "16" + ] + }, + { + "qid": "ins_a_020", + "domain": "insurance", + "split": "A", + "question": "王某投保了平安e生保（有医保）、太保团体百万医疗（有医保）、众安家财险。某日家中因火灾造成地板损坏，维修费用8000元；同时王某因修理时不慎摔伤，门诊费用2000元（医保未报销）。以下赔付结果正确的是？", + "options": { + "A": "家财险赔8000元；医疗险：e生保赔1200元，太保赔1200元，合计2400元", + "B": "家财险赔8000元；医疗险：e生保和太保按比例分摊，共赔2000元", + "C": "家财险赔8000元；医疗险：e生保赔1200元，太保赔480元，合计1680元", + "D": "家财险赔8000元；医疗险：e生保和太保均不赔付" + }, + "answer_format": "mcq", + "type": "推理判断", + "doc_ids": [ + "5", + "6", + "12" + ] + } +] \ No newline at end of file diff --git a/data/questions/group_a/regulatory_questions.json b/data/questions/group_a/regulatory_questions.json new file mode 100644 index 0000000..b1d64a3 --- /dev/null +++ b/data/questions/group_a/regulatory_questions.json @@ -0,0 +1,352 @@ +[ + { + "qid": "reg_a_001", + "domain": "regulatory", + "split": "A", + "question": "关于《金融机构客户受益所有人识别管理办法》与《金融机构客户尽职调查和客户身份资料及交易记录保存管理办法》的实施要求，下列说法正确的有？", + "options": { + "A": "受益所有人信息核对发现重大差异的，金融机构应在发现之日起 30 个工作日内提交差异报告", + "B": "若无法按规定开展客户尽职调查，金融机构可先建立业务关系但需提交可疑交易报告", + "C": "存量较高风险以上客户的受益所有人识别核实工作应自《金融机构客户受益所有人识别管理办法》施行之日起 6 个月内完成", + "D": "《金融机构客户尽职调查和客户身份资料及交易记录保存管理办法》的施行日期早于《金融机构客户受益所有人识别管理办法》" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "strict_v3_008_中国人民银行令〔2025〕第12号（金融机构客户受益所有人识别管理办法）", + "strict_v3_009_中国人民银行_国家金融监督管理总局_中国证券监督管理委员会令〔2025〕第11号（金融机构客户尽职调查和客户身份资料及交易记录保存管理办法）" + ] + }, + { + "qid": "reg_a_002", + "domain": "regulatory", + "split": "A", + "question": "结合《中华人民共和国反洗钱法》与《金融机构客户受益所有人识别管理办法》，下列关于金融机构义务的说法中，正确的有？", + "options": { + "A": "客户身份资料在业务关系结束后，应当至少保存十年", + "B": "受益所有人备案信息不准确导致重大差异的，金融机构应在发现差异之日起 30 个工作日内提交差异报告", + "C": "依法履行反洗钱职责获得的客户身份资料，非依法律规定不得向任何单位和个人提供", + "D": "存量全部客户的受益所有人识别核实工作应自办法施行之日起 1 年内完成" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "strict_v3_008_中国人民银行令〔2025〕第12号（金融机构客户受益所有人识别管理办法）", + "strict_v3_017_中华人民共和国反洗钱法" + ] + }, + { + "qid": "reg_a_003", + "domain": "regulatory", + "split": "A", + "question": "根据相关法规，金融机构在处理上市公司受益所有人身份信息时，因包含照片等敏感数据项，原则上不得在终端设备中存储，确需存储的应当统一规范管理。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "strict_v3_008_中国人民银行令〔2025〕第12号（金融机构客户受益所有人识别管理办法）", + "strict_v3_015_中国人民银行令〔2025〕第3号（中国人民银行业务领域数据安全管理办法）" + ] + }, + { + "qid": "reg_a_004", + "domain": "regulatory", + "split": "A", + "question": "关于金融机构及银行卡清算机构的报告时限要求，下列说法正确的有？", + "options": { + "A": "银行卡清算机构董事和高级管理人员职务变动的，应当自变动之日起 7 日内向监管部门报告", + "B": "金融机构查询核对受益所有人信息发现重大差异的，应当在发现之日起 30 个工作日内提交差异报告", + "C": "银行卡清算机构撤并分支机构的，应当至少提前 30 日向住所地中国人民银行分支机构报告", + "D": "金融机构对存量全部非自然人客户的受益所有人识别核实工作，应自办法施行之日起 6 个月内完成" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "strict_v3_008_中国人民银行令〔2025〕第12号（金融机构客户受益所有人识别管理办法）", + "strict_v3_016_中国人民银行_国家金融监督管理总局令〔2025〕第2号（银行卡清算机构管理办法）" + ] + }, + { + "qid": "reg_a_005", + "domain": "regulatory", + "split": "A", + "question": "结合《金融机构客户受益所有人识别管理办法》与《非银行支付机构监督管理条例实施细则》，以下说法正确的有？", + "options": { + "A": "非银行支付机构高级管理人员在同一机构内调任其他职位的，无需提交变更申请，但应于变更后 10 日内报告", + "B": "对于上市公司受益所有人，金融机构识别留存的身份信息应当包括可以用于身份识别的照片", + "C": "非银行支付机构调整收费标准的，原则上应当至少于调整施行前 30 个自然日公示", + "D": "金融机构判断为受益所有人信息非重大差异的，应当在发现差异之日起 30 个工作日内通过系统提交差异报告" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "strict_v3_008_中国人民银行令〔2025〕第12号（金融机构客户受益所有人识别管理办法）", + "strict_v3_018_中国人民银行令〔2024〕第4号（非银行支付机构监督管理条例实施细则）" + ] + }, + { + "qid": "reg_a_006", + "domain": "regulatory", + "split": "A", + "question": "根据相关规定，金融机构对存量高风险非自然人客户的受益所有人识别核实工作应自新规施行之日起 6 个月内完成，且上市公司定期报告在未经董事会审议通过前不得披露。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断", + "doc_ids": [ + "csrc_0009_att1", + "strict_v3_008_中国人民银行令〔2025〕第12号（金融机构客户受益所有人识别管理办法）" + ] + }, + { + "qid": "reg_a_007", + "domain": "regulatory", + "split": "A", + "question": "关于《金融机构客户尽职调查和客户身份资料及交易记录保存管理办法》与《中华人民共和国反洗钱法》的相关规定，下列说法正确的有？", + "options": { + "A": "新办法自 2026 年 1 月 1 日起施行，同时废止了 2007 年和 2022 年发布的旧版客户尽职调查相关规定", + "B": "客户身份资料在业务关系结束后应当至少保存五年", + "C": "第三方具有较高风险情形或者不具备履行反洗钱义务能力的，金融机构不得依托其开展客户尽职调查", + "D": "保险公司办理保单贷款金额超过人民币 1 万元时，应当核实申请人身份" + }, + "answer_format": "multi", + "type": "多选", + "doc_ids": [ + "strict_v3_009_中国人民银行_国家金融监督管理总局_中国证券监督管理委员会令〔2025〕第11号（金融机构客户尽职调查和客户身份资料及交易记录保存管理办法）", + "strict_v3_017_中华人民共和国反洗钱法" + ] + }, + { + "qid": "reg_a_008", + "domain": "regulatory", + "split": "A", + "question": "依据《银行卡清算机构管理办法》和《金融机构客户尽职调查和客户身份资料及交易记录保存管理办法》，下列哪项表述符合监管要求？", + "options": { + "A": "银行卡清算机构的董事和高级管理人员停止任职的，应当自职务变动之日起 7 日内向监管机构报告", + "B": "金融机构无法开展客户尽职调查时，仅需终止业务关系，无需提交可疑交易报告", + "C": "银行卡清算机构撤并分支机构的，应当至少提前 7 日向住所地中国人民银行分支机构报告", + "D": "保险公司在客户办理保单贷款金额达到 1000 美元以上时，才需要核实申请人身份" + }, + "answer_format": "mcq", + "type": "单选", + "doc_ids": [ + "strict_v3_009_中国人民银行_国家金融监督管理总局_中国证券监督管理委员会令〔2025〕第11号（金融机构客户尽职调查和客户身份资料及交易记录保存管理办法）", + "strict_v3_016_中国人民银行_国家金融监督管理总局令〔2025〕第2号（银行卡清算机构管理办法）" + ] + }, + { + "qid": "reg_a_009", + "domain": "regulatory", + "split": "A", + "question": "结合《中国人民银行业务领域数据安全管理办法》与《金融机构客户尽职调查和客户身份资料及交易记录保存管理办法》，以下说法正确的是？", + "options": { + "A": "重要数据的处理者应当于每年 1 月 15 日前报送上一年度风险评估报告", + "B": "向其他数据处理者提供核心数据达到国家规定情形的，事前应经中国人民银行报国家数据安全工作协调机制开展风险评估", + "C": "客户身份资料在业务关系结束后，可以立即删除以满足数据最小化原则", + "D": "发生业务数据安全事件造成危害后果时，若证明已按规定采取措施并及时补救，可依法获得从轻或减轻行政处罚" + }, + "answer_format": "multi", + "type": "多选", + "doc_ids": [ + "strict_v3_009_中国人民银行_国家金融监督管理总局_中国证券监督管理委员会令〔2025〕第11号（金融机构客户尽职调查和客户身份资料及交易记录保存管理办法）", + "strict_v3_015_中国人民银行令〔2025〕第3号（中国人民银行业务领域数据安全管理办法）" + ] + }, + { + "qid": "reg_a_010", + "domain": "regulatory", + "split": "A", + "question": "按照现行规定，非银行支付机构董事、监事在同一机构内调任职位的，应当于变更完成后 10 日内向所在地人民银行分支机构报告；同时，对于无法按规开展客户尽职调查的金融机构，应当终止已建立的业务关系并向反洗钱监测分析机构提交大额交易报告。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断", + "doc_ids": [ + "strict_v3_009_中国人民银行_国家金融监督管理总局_中国证券监督管理委员会令〔2025〕第11号（金融机构客户尽职调查和客户身份资料及交易记录保存管理办法）", + "strict_v3_018_中国人民银行令〔2024〕第4号（非银行支付机构监督管理条例实施细则）" + ] + }, + { + "qid": "reg_a_011", + "domain": "regulatory", + "split": "A", + "question": "根据相关监管规定，下列哪些情形需要履行特定的内部审批程序或满足特定金额门槛？", + "options": { + "A": "上市公司年度报告若未经董事会审议通过，则不得对外披露", + "B": "金融机构在与空壳银行建立代理行关系时，只要获得董事会批准即可开展业务", + "C": "当客户申请解除保险合同且退还金额为人民币 1 万元以上时，保险公司需核实身份", + "D": "若反洗钱调查在最低保存期限届满时仍未结束，相关记录应保存至调查结束" + }, + "answer_format": "multi", + "type": "multi", + "doc_ids": [ + "csrc_0038_att1", + "strict_v3_009_中国人民银行_国家金融监督管理总局_中国证券监督管理委员会令〔2025〕第11号（金融机构客户尽职调查和客户身份资料及交易记录保存管理办法）" + ] + }, + { + "qid": "reg_a_012", + "domain": "regulatory", + "split": "A", + "question": "关于上市公司治理及年度报告编制，以下说法符合规定的有哪些？", + "options": { + "A": "上市公司章程及与治理相关的文件应当符合《上市公司治理准则》的要求", + "B": "年度报告披露后存在退市风险警示情形的公司，应当披露相关原因", + "C": "上市公司可以在不具备分红条件而不进行现金分红的情况下，不披露具体原因", + "D": "上市公司应当在股东会召开前披露董事候选人" + }, + "answer_format": "multi", + "type": "multi", + "doc_ids": [ + "csrc_0023_att1", + "csrc_0038_att1" + ] + }, + { + "qid": "reg_a_013", + "domain": "regulatory", + "split": "A", + "question": "上市公司应当在股东会召开前披露董事候选人，且半年度报告内容应当经上市公司董事会审议通过后方可披露。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "tf", + "doc_ids": [ + "csrc_0023_att1", + "csrc_0037_att1" + ] + }, + { + "qid": "reg_a_014", + "domain": "regulatory", + "split": "A", + "question": "结合上市公司治理准则与章程指引，下列关于股东大会职权及会议规则的描述正确的有哪些？", + "options": { + "A": "股东大会有权审议批准董事会的报告", + "B": "上市公司应当在股东会召开前披露董事候选人", + "C": "公司章程规定的担保事项无需经过股东大会审议批准", + "D": "股东大会职权包括审议批准变更募集资金用途事项" + }, + "answer_format": "multi", + "type": "multi", + "doc_ids": [ + "csrc_0023_att1", + "csrc_0035_att1" + ] + }, + { + "qid": "reg_a_015", + "domain": "regulatory", + "split": "A", + "question": "根据《上市公司信息披露管理办法》和《上市公司治理准则》，关于定期报告与利润分配的规定，下列哪项表述是准确的？", + "options": { + "A": "定期报告仅包含年度报告，不包含半年度报告", + "B": "上市公司未在规定期限内披露年度报告和中报的，无需承担法律责任", + "C": "上市公司在具备分红条件但不进行现金分红时，应当充分披露原因", + "D": "定期报告中的财务信息无需经过审计即可披露" + }, + "answer_format": "mcq", + "type": "mcq", + "doc_ids": [ + "csrc_0009_att1", + "csrc_0023_att1" + ] + }, + { + "qid": "reg_a_016", + "domain": "regulatory", + "split": "A", + "question": "结合上市公司治理规范与审计机构行政处罚案例，关于公司治理及审计责任，下列说法正确的有？", + "options": { + "A": "上市公司应当在股东会召开前披露董事候选人情况", + "B": "若上市公司具备现金分红条件而不进行分红的，应当充分披露原因", + "C": "会计师事务所因未勤勉尽责受到行政处罚的，证监会应以名义业务收入为基数作出处罚决定", + "D": "签字注册会计师在年度报告审计中未勤勉尽责，可能面临行政处罚" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "csrc_0023_att1", + "csrc_0271" + ] + }, + { + "qid": "reg_a_017", + "domain": "regulatory", + "split": "A", + "question": "关于上市公司治理规范及行政处罚时效的相关规定，下列说法中正确的有哪些？", + "options": { + "A": "上市公司章程及与治理相关的文件应当符合本准则的要求", + "B": "董事候选人的相关信息应当在股东会召开之前予以披露", + "C": "若信息披露违法不存在连续继续状态，则该信息已超过处罚时效", + "D": "现金分红必须在年度审计报告出具前完成支付" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "csrc_0023_att1", + "csrc_0262" + ] + }, + { + "qid": "reg_a_018", + "domain": "regulatory", + "split": "A", + "question": "判断题：根据相关监管规定，上市公司定期报告须经董事会审议通过后方可披露，且证券公司因重大违法违规被实施行政处罚的，其分类评价得分将受到相应扣减。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "csrc_0009_att1", + "csrc_0027_att1" + ] + }, + { + "qid": "reg_a_019", + "domain": "regulatory", + "split": "A", + "question": "结合证券公司分类监管规定与市场禁入决定书的案例背景，以下关于违规处理及后果的说法中，正确的有？", + "options": { + "A": "证券公司分类评价新规自 2025 年 8 月 22 日起施行", + "B": "涉及信息披露违法违规的重大资产重组文件应披露关联交易事项", + "C": "被中国证监会实施行政处罚的证券公司，其分类评价得分不会受到影响", + "D": "金洲慈航时任董事长朱要文因主导重大资产重组信息披露违法行为被认定为直接负责的主管人员" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "csrc_0027_att1", + "csrc_0377" + ] + }, + { + "qid": "reg_a_020", + "domain": "regulatory", + "split": "A", + "question": "依据相关准则与规定，关于半年度报告的编制要求及监管规定的生效时点，下列描述准确的有？", + "options": { + "A": "半年度报告内容应当经上市公司董事会审议通过", + "B": "未经董事会审议通过的半年度报告不得披露", + "C": "证券公司分类监管规定于 2025 年 8 月 22 日停止施行", + "D": "相关分类监管规定的施行时间为 2025 年 8 月 22 日" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "csrc_0027_att1", + "csrc_0037_att1" + ] + } +] \ No newline at end of file diff --git a/data/questions/group_a/research_questions.json b/data/questions/group_a/research_questions.json new file mode 100644 index 0000000..a88932d --- /dev/null +++ b/data/questions/group_a/research_questions.json @@ -0,0 +1,352 @@ +[ + { + "qid": "res_a_001", + "domain": "research", + "split": "A", + "question": "关于银保渠道发展历程及银行 IT 市场规模的描述，下列哪些选项是正确的？", + "options": { + "A": "韩国寿险银保渠道保费贡献率在 2022 年达到了 56%", + "B": "2005 年至 2018 年期间，银保渠道实现复合增速 9.9%", + "C": "2025 年金融信创市场规模预计接近 2500 亿元", + "D": "宇信科技 2025 年营收同比增长 8.47%" + }, + "answer_format": "multi", + "type": "industry_comparison", + "doc_ids": [ + "pack2_text01", + "pack2_text17" + ] + }, + { + "qid": "res_a_002", + "domain": "research", + "split": "A", + "question": "结合两份报告内容，关于行业市场规模及增速的预测，以下说法正确的是？", + "options": { + "A": "2030 年全球光通信市场规模预计将达到 900 亿美元", + "B": "韩国寿险银保渠道保费贡献率在过去 20 年复合增速达到 12%", + "C": "2029 年中国 ICT 市场规模预计接近 8894.3 亿美元", + "D": "2025 年金融信创市场规模预计接近 2500 亿元" + }, + "answer_format": "multi", + "type": "market_forecast", + "doc_ids": [ + "pack2_text01", + "pack2_text11" + ] + }, + { + "qid": "res_a_003", + "domain": "research", + "split": "A", + "question": "判断以下陈述是否正确：韩国寿险银保渠道近 20 年的复合增速低于远望谷在新兴赛道的 RFID 标签出货量复合增速。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "data_verification", + "doc_ids": [ + "pack2_text01", + "pack2_text19" + ] + }, + { + "qid": "res_a_004", + "domain": "research", + "split": "A", + "question": "关于芯原股份的业务模式及银保渠道发展情况，下列描述符合文档内容的是？", + "options": { + "A": "芯原股份主要提供芯片定制服务和半导体 IP 授权服务", + "B": "2030 年数据中心半导体加速市场规模预计将达 4930 亿美元", + "C": "韩国寿险银保渠道保费贡献超过 50%，支撑了韩国寿险管理体系", + "D": "芯原股份 2024 年 IP 授权业务市场份额全球第一" + }, + "answer_format": "multi", + "type": "business_model", + "doc_ids": [ + "pack2_text01", + "pack2_text09" + ] + }, + { + "qid": "res_a_005", + "domain": "research", + "split": "A", + "question": "基于消费与保险行业报告，关于经济增长指标及渠道贡献的说法，正确的有？", + "options": { + "A": "截至 2025 年 12 月，服务零售累计同比高出商品零售增速", + "B": "韩国寿险银保渠道保费贡献率超过 50%", + "C": "2022 年居民可支配收入增速降至 5%", + "D": "2025 年居民收入增速与人均名义 GDP 增速剪刀差由负转正" + }, + "answer_format": "multi", + "type": "economic_indicator", + "doc_ids": [ + "pack2_text01", + "pack2_text03" + ] + }, + { + "qid": "res_a_006", + "domain": "research", + "split": "A", + "question": "判断以下陈述是否正确：2008 年至 2025 年前三季度，我国上市券商客户资金杠杆从 1.56 倍提升至 4.09 倍，同期自有资产净利率由 4.3% 降至 1.8%。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "financial_ratio", + "doc_ids": [ + "pack2_text01", + "pack2_text10" + ] + }, + { + "qid": "res_a_007", + "domain": "research", + "split": "A", + "question": "关于网络安全运营及银行 IT 企业的财务表现，下列描述准确的有？", + "options": { + "A": "网络安全运营数字化底座提供了超过 1000 条内置检测规则", + "B": "2025 年金融信创市场规模预计接近 2500 亿元", + "C": "形成 3384 条解析规则实现自动解析", + "D": "宇信科技 2025 年前三季度净利润盈利 1139.39 万元" + }, + "answer_format": "multi", + "type": "tech_standard", + "doc_ids": [ + "pack2_text02", + "pack2_text17" + ] + }, + { + "qid": "res_a_008", + "domain": "research", + "split": "A", + "question": "下列关于网络安全标准及光通信市场规模的描述，哪一项是正确的？", + "options": { + "A": "网络安全运营数字化底座提供的内置检测规则超过 1000 条", + "B": "2030 年全球光通信市场规模预计将达到 9000 亿美元", + "C": "2029 年中国 ICT 市场规模预计接近 8894.3 亿元人民币", + "D": "网络安全对象标准超 1200 项" + }, + "answer_format": "mcq", + "type": "data_accuracy", + "doc_ids": [ + "pack2_text02", + "pack2_text11" + ] + }, + { + "qid": "res_a_009", + "domain": "research", + "split": "A", + "question": "结合银保渠道历史与网络安全运营数据，以下说法成立的有？", + "options": { + "A": "韩国寿险银保渠道保费贡献超过 50%", + "B": "网络安全运营数字化底座提供了超过 1000 条内置检测规则", + "C": "欧盟银保渠道原保费全球市场份额从 1985 年的 10% 快速提升", + "D": "形成 3384 条解析规则实现手动解析" + }, + "answer_format": "multi", + "type": "cross_industry", + "doc_ids": [ + "pack2_text01", + "pack2_text02" + ] + }, + { + "qid": "res_a_010", + "domain": "research", + "split": "A", + "question": "判断以下陈述是否正确：根据文档信息，上周新成立基金份额为 91.1 亿份，其中主动型新发 56.3 亿份。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "fund_flow", + "doc_ids": [ + "pack2_text02", + "pack2_text08" + ] + }, + { + "qid": "res_a_011", + "domain": "research", + "split": "A", + "question": "关于券商杠杆变化及安全运营规则的统计，下列选项正确的有？", + "options": { + "A": "2008 年至 2025Q1-3 我国上市券商客户资金杠杆从 1.56 倍提升至 4.09 倍", + "B": "网络安全运营数字化底座提供了超过 1000 条内置检测规则", + "C": "同期自有资产净利率由 4.3% 降至 1.8%", + "D": "形成 3384 条解析规则实现手动解析" + }, + "answer_format": "multi", + "type": "brokerage_security", + "doc_ids": [ + "pack2_text02", + "pack2_text10" + ] + }, + { + "qid": "res_a_012", + "domain": "research", + "split": "A", + "question": "关于芯片设计服务与安全运营数据的描述，符合文档内容的有？", + "options": { + "A": "芯原股份依托自主半导体 IP，为客户提供芯片定制服务", + "B": "网络安全运营数字化底座提供了超过 1000 条内置检测规则", + "C": "2030 年数据中心半导体加速市场规模将达 4930 亿美元", + "D": "形成 3384 条解析规则实现手动解析" + }, + "answer_format": "multi", + "type": "chip_security", + "doc_ids": [ + "pack2_text02", + "pack2_text09" + ] + }, + { + "qid": "res_a_013", + "domain": "research", + "split": "A", + "question": "判断以下陈述是否正确：2025 年我国宠物医疗市场规模为人民币 2786 亿元，占总体的 30.2%。", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "pet_market", + "doc_ids": [ + "pack2_text03", + "pack2_text13" + ] + }, + { + "qid": "res_a_014", + "domain": "research", + "split": "A", + "question": "关于服务消费趋势及芯片市场预测，以下说法正确的有？", + "options": { + "A": "截至 2025 年 12 月，服务零售增速持续领跑商品零售", + "B": "芯原股份主要客户包括芯片设计公司、IDM、系统厂商等", + "C": "2022 年居民可支配收入增速降至 5%", + "D": "2030 年数据中心半导体加速市场规模将达 4930 亿欧元" + }, + "answer_format": "multi", + "type": "consumption_chip", + "doc_ids": [ + "pack2_text03", + "pack2_text09" + ] + }, + { + "qid": "res_a_015", + "domain": "research", + "split": "A", + "question": "基于金融机构配置线索及服务消费报告，下列哪项数据描述是准确的？", + "options": { + "A": "2025 年上市险企共计实现归母净利润 4252.91 亿元", + "B": "2025 年冰雪装备市场规模已达 8466 亿元", + "C": "2025 年存款定期化趋势放缓，M1-M2 增速差持续扩大", + "D": "2025 年上市银行手续费及佣金净收入增速持续下降" + }, + "answer_format": "mcq", + "type": "bank_insurance", + "doc_ids": [ + "pack2_text03", + "pack2_text14" + ] + }, + { + "qid": "res_a_016", + "domain": "research", + "split": "A", + "question": "根据提供的两份报告，关于 2025 年至 2026 年初的经济与金融趋势，以下哪些描述得到了数据支持？", + "options": { + "A": "截至 2025 年 12 月，服务零售累计同比增速高于商品零售", + "B": "2025 年上市银行手续费及佣金净收入增速出现明显负增长", + "C": "2023 年至 2025 年间居民可支配收入增速从 6.33% 下降至 4.99%", + "D": "受资本市场震荡影响，上市险企 2025 年四季度单季利润普遍承压" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "pack2_text03", + "pack2_text20" + ] + }, + { + "qid": "res_a_017", + "domain": "research", + "split": "A", + "question": "结合产业前瞻与银行 IT 深度报告，关于 2025 年的市场容量或财务表现，以下哪项陈述是准确的？", + "options": { + "A": "2025 年金融信创市场规模预计接近 2500 亿元", + "B": "由于高储蓄并存，我国服务消费占比仍处相对低位", + "C": "天阳科技 2025 年全年实现了显著的净利润同比增长", + "D": "2023 年至 2025 年居民收入增速呈现持续放缓趋势" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "pack2_text03", + "pack2_text17" + ] + }, + { + "qid": "res_a_018", + "domain": "research", + "split": "A", + "question": "根据电动车行业策略与车展前瞻报告，2026 年 3 月乘用车新能源渗透率为 51.5%，且 2026 年 1 月宁德时代市占率回升至 25%，该说法是否正确？", + "options": { + "A": "正确", + "B": "错误" + }, + "answer_format": "tf", + "type": "判断题", + "doc_ids": [ + "pack2_text04", + "pack2_text07" + ] + }, + { + "qid": "res_a_019", + "domain": "research", + "split": "A", + "question": "基于化工周报与电动车行业策略报告，关于大宗商品价格对行业的影响及估值预测，以下哪些判断是正确的？", + "options": { + "A": "若碳酸锂价格为 15 万元，2026 年权益资源利润对应 PE 估值为 10-15 倍", + "B": "美伊谈判释放缓和信号导致油价下滑，进而带动化工品价格整体回落", + "C": "2026 年一季度国内电动车累计销量同比增长 3.6%", + "D": "伊朗于 4 月 17 日无条件开放了霍尔木兹海峡" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "pack2_text04", + "pack2_text06" + ] + }, + { + "qid": "res_a_020", + "domain": "research", + "split": "A", + "question": "综合电动车行业策略与集成电路深度报告，关于技术赛道预测及锂电估值，以下哪些信息是准确的？", + "options": { + "A": "芯原股份截至 2025 年统计了 2024 年 IP 授权业务市场份额", + "B": "2028-2029 年若碳酸锂价格为 15 万元，权益资源利润对应 PE 估值为 5-10 倍", + "C": "芯原股份预测 FY27 AI 芯片将带来千亿收入", + "D": "2026 年一季度国内电动车销量同比下降 3.6%" + }, + "answer_format": "multi", + "type": "多选题", + "doc_ids": [ + "pack2_text04", + "pack2_text09" + ] + } +] \ No newline at end of file diff --git a/docs/AFAC2026_FINAL_EXECUTION_PROMPT.md b/docs/AFAC2026_FINAL_EXECUTION_PROMPT.md new file mode 100644 index 0000000..155032b --- /dev/null +++ b/docs/AFAC2026_FINAL_EXECUTION_PROMPT.md @@ -0,0 +1,355 @@ +# AwareLiquid-M2 最终执行总控 Prompt + +## 0. 当前状态与物理启动门 + +本文件是后续执行的完整 Prompt。当前回合仅允许读取和审查本文件；不得修改项目代码，不得创建或运行 E/V/R/Sol Agent，不得调用 Qwen/API、外网或 API Key，不得生成、上传或提交 `answer.csv`。 + +只有用户明确发送 `CONFIRM_IMPLEMENTATION`，或同等明确且不可误解地授权“进入实现阶段”后，Controller 才能创建 A0 `stage-contract`、Packet、Manifest 和 sidecar，并派遣 A0。 + +在收到确认前，严禁： + +- 修改项目代码； +- 创建或写入执行 Packet、stage-contract、Manifest 或 sidecar； +- 启动 E/V/R/Sol Agent； +- 运行实现、测试、格式化、安装或迁移命令； +- 调用 Qwen/API、外网或 API Key； +- 生成、上传或提交 `answer.csv`。 + +目标是在保留当前 working tree 中有意义的 dirty changes 的前提下，按 A0-A6 修复工程契约、确定性证据链、Qwen 正式模式和候选预检。无关 dirty changes 必须保护，不得覆盖、删除或 reset。 + +阶段含义固定： + +- A0：只读规则、代码、数据基线审计； +- A1：输入、答案和 CSV 契约； +- A2：Qwen provider、network、Mock、Token gate； +- A3：允许的离线预处理与 Evidence provenance； +- A4：Evidence 组织、Decimal、选项验证和 deterministic exact-only locator； +- A5：用户授权后的 Qwen 正式模式和本地候选； +- A6：独立只读最终验收，不生成候选。 + +## 1. 唯一规则来源与硬约束 + +唯一规范性来源： + +`reference/AFAC2026_OFFICIAL_RULES.md` + +`output/`、其他 `reference/` 文件、handoff、README、旧 Prompt、旧 manifest、代码和测试只能作为思路、baseline 经验和实现启发，不能定义或覆盖正式字段、提交格式、模型限制、A/B 规则或时间。冲突必须舍弃并报告，禁止猜测。 + +硬约束： + +- 正式推理只能使用官方允许的 Qwen API。当前官网目标若为 Qwen3.6-plus，精确 model id 必须由用户确认且 API 实际可用；禁止猜测、替换非 Qwen、静默降级或 Mock fallback。 +- OCR、PDF/HTML/TXT 解析、版面分析、表格恢复、阅读顺序还原和结构化文本转换等非 Qwen 预处理允许使用。 +- 非 Qwen 不得生成直接用于正式答题的语义摘要、FAQ、结论或知识库。 +- 正式答题禁止 Embedding、向量召回及其排序、非 Qwen rerank、非 Qwen 候选过滤、非 Qwen 投票/纠错。 +- 确定性关键词、章节、条款、数字、日期、标题、表格、doc_id 和原文位置可用于证据定位。 +- A=100，B=100。 +- A 时间为 `2026-06-08 10:00–2026-07-21 20:00`。 +- B 时间为 `2026-07-22 00:00–2026-07-24 17:00`。 +- A 有效成绩前不得进入 B。 +- A 每题必须使用题目提供的非空 doc_ids；A locator 严格限定这些 doc_ids。 +- B 无 doc_ids，只有官方 B 文件出现后才可启用 B locator。 +- 当前本地只有官方 A100，无官方 B；只能使用明确标记的 synthetic B 非赛事 dry-run，不得从 A 删除 doc_ids 伪造 B，不得声称真实 B 验证。 +- TokenBudget 为 5,000,000；所有模型调用（规划、检索摘要、上下文压缩、证据判断、答案生成、自检）进入同一 ledger；usage 缺失、未知或超预算必须 fail closed。 +- CSV 按官方文件执行：UTF-8、<100MB；第一行 `qid,answer,prompt_tokens,completion_tokens,total_tokens`；第二行 `summary,,prompt_total,completion_total,total_total`；无 `unused_tokens`；qid 唯一且题数完整；`total_tokens = prompt_tokens + completion_tokens`；mcq 输出大写选项；multi 去重、按字母排序、无分隔符；tf 按 options 输出 A/B，不硬编码 T/F。 +- 没有 A 标准答案 key 不报告真实 A accuracy；没有官方 B 不报告真实 B accuracy；Mock、GPT 子 Agent、非 Qwen 模型不是赛事结果。 + +## 2. Stage Contract、模型探测、hash 和派遣协议 + +### 2.1 模型探测 + +每个 stage-contract 创建前，Controller 必须在不调用 Qwen/API/外网的本地控制环境执行一次 Luna availability probe，并记录绝对 cwd、argv、env、退出码、结果、model、model_hash、时间和决策。 + +- Luna 可用：该阶段 E/V/R 全部使用同一 `gpt-5.6-luna`； +- Luna 不可用：该阶段 E/V/R 全部使用同一 `gpt-5.6-terra`； +- 同一阶段不得混用 Luna/Terra； +- A6 后的 `FINAL-PROJECT-V-SOL` 使用新的独立 `gpt-5.6-sol`； +- Sol 不可用时 STOP/BLOCKED，不回退； +- probe 失败、结果不确定或 model_hash 无法记录时，不创建 contract、不派 Agent，标记 `BLOCKED_MODEL_PROBE`。 + +### 2.2 Stage Contract + +收到 `CONFIRM_IMPLEMENTATION` 后，每个 `run_id + stage` 只创建一个不可变、versioned `stage-contract.json` 和 `stage-contract.json.sha256`。E/V/R 必须共同引用同一 `contract_sha256`。 + +Contract 必须绑定： + +- run_id、thread_id、stage； +- rules_path、rules_sha256； +- model_probe、model、model_hash、decision； +- git HEAD、status、diff-stat、dirty 文件 hashes； +- inputs（path/hash/purpose）； +- 完整 E/V/R commands； +- acceptance criteria、fail conditions、stop conditions； +- read/write/forbidden permissions； +- artifacts、handoff。 + +任何规则、baseline、AC、fail、stop、scope、model、command、allowlist 或 artifact policy 改变，都必须新 run_id、新 contract、新 E；V/R 不得修改 contract。 + +### 2.3 Canonical Hash + +所有 contract、Packet、Manifest canonical JSON 的 hash domain 固定为： + +- UTF-8、无 BOM、LF、无尾随空格； +- 对象键递归按 Unicode code-point 升序； +- 数组保持原顺序； +- 仅 JSON 标准类型； +- 数字唯一表示，禁止 NaN/Infinity； +- separator 只用 `,` 与 `:`； +- canonical body 末尾不含 LF； +- 落盘最多追加一个 LF，hash 排除该 LF。 + +hash 排除自身或相互自引用字段： + +`contract_sha256`、`packet_sha256`、`prompt_sha256`、`manifest_sha256`、`self_hash`、`created_at`、`status`、`result`。 + +Sidecar 固定单行格式： + +```text +<64位小写hex><两个ASCII空格>\n +``` + +Sidecar 必须 create-exclusive，已存在不得覆盖。固定文件名： + +```text +stage-contract.json +stage-contract.json.sha256 +packet.json +packet.json.sha256 +prompt.md +prompt.md.sha256 +manifest.json +manifest.json.sha256 +``` + +Prompt 使用 UTF-8、LF、无 BOM、无尾随空格、末尾恰好一个 LF；Prompt hash 是完整 role Prompt UTF-8 bytes，sidecar 不参与自身 hash。 + +### 2.4 Command Object + +每个 E/V/R/Sol 命令必须是完整对象，不得“见上文”、省略号或让 Agent 自行发明： + +```json +{ + "id": "A6-V-CMD-01", + "cwd": "绝对 realpath", + "argv": ["每个参数单独元素"], + "env_inheritance": "none", + "env_allow": {}, + "env_deny": ["HTTP_PROXY", "HTTPS_PROXY", "ALL_PROXY", "http_proxy", "https_proxy", "all_proxy"], + "stdin": {"source": "none|literal|absolute-file", "sha256": "..."}, + "network_enforcement": { + "mode": "actual-deny|allowlisted-qwen-only", + "egress_allowlist": [], + "preflight_argv": [], + "verify_argv": [], + "expected_preflight_exit": 0, + "expected_verify_exit": 0 + }, + "credential_ref": null, + "proxy_unset": true, + "timeout_seconds": 300, + "kill_grace_seconds": 5, + "allowed_read_roots": [], + "allowed_writes": [], + "expected_exit_codes": [0], + "expected_artifacts": [] +} +``` + +禁止 shell、pipe、redirection、命令替换、隐式子进程和隐式网络。A0-A4 及 A5 授权前使用 `actual-deny`；A5 授权后仅使用 `allowlisted-qwen-only`；A6 只读、无网络。 + +### 2.5 Allowlist 与依赖闭包 + +所有 allowlist 元素必须是 `{path,type,recursive,access}` 对象。使用前执行 realpath+lstat，拒绝 symlink escape、目录穿越、设备文件、FIFO 和未知类型；forbidden 优先；每次 open/read/write 重算 realpath；未分类路径一律 BLOCKED_SCOPE。 + +必须审计 Python import、dynamic import、plugin、dlopen、子进程和配置加载的依赖闭包。禁止加载 embedding、sentence-transformers、transformers、torch、SentenceEncoder、vector DB、dense/hybrid、RRF、rerank。 + +V 的缓存、SQLite、报告和临时文件只能写： + +`/tmp/awareliquid-verification//` + +并设置 `PYTHONDONTWRITEBYTECODE=1`、`TMPDIR`、`pytest -p no:cacheprovider`。 + +### 2.6 Packet/Prompt/Manifest 闭环 + +hash 依赖顺序固定： + +```text +contract → packet → prompt → manifest +``` + +Prompt 必须嵌入与外部 Packet 完全逐字节一致的： + +```text +BEGIN PACKET +... +END PACKET +``` + +抽取后重算 hash 必须与外部 Packet sidecar 一致。每个 Agent 启动前重新计算 contract、packet、prompt、manifest digest，并核对 sidecar、内嵌 Packet、realpath、model probe。任何 mismatch、CRLF、额外尾行、sidecar 可覆盖、contract/model 不一致都标记 `BLOCKED_HASH_MISMATCH`，不得执行。 + +E 完成后派新 V；V FAIL 才派新 R；R 后新 V 全量复验。V 不信任 E/R 报告。R 不得修改 contract、AC、fail、scope、model、commands 或 allowlist。 + +## 3. 统一报告 + +每份 E/V/R/Sol 报告至少包含： + +```yaml +run_id: ... +thread_id: ... +stage: ... +role: ... +model: ... +model_probe: ... +contract_sha256: ... +packet_sha256: ... +prompt_sha256: ... +manifest_sha256: ... +status: PASS|FAIL|BLOCKED +rules_source: reference/AFAC2026_OFFICIAL_RULES.md +rules_sha256: ... +commands: [] +changed_files: [] +findings: [] +artifacts: [] +next_action: ... +accuracy_claim: "不得声称真实赛事 accuracy" +``` + +V 必须按每个 AC ID 提供 PASS/FAIL 和直接证据。 + +## 4. A0：只读基线审计 + +E-A0 只读官方规则、HEAD/status/diff-stat、代码、测试、manifest、data 和思路材料，记录 baseline/hash/data 摘要，检查 unused_tokens、summary、CSV 表头、tf T/F、Mock fallback、provider/model gate、embedding/hybrid/dense/rerank、A doc_ids、Evidence、ledger、A/B 数据与时间偏差。不得修改、联网、调用 API、生成 CSV 或报告 accuracy。 + +V-A0 独立重做审计和 hash 核对，确认 E 无写入、无网络，每项有直接证据。 + +R-A0 只能补正审计目录中的报告、manifest、证据；不得改项目、规则或数据；新 V-A0 全量复验。 + +## 5. A1：CSV/输入输出契约 + +E-A1 在 A0 V PASS 后修 canonical contract、schemas、submit 和测试，实现官方表头、summary、五列 Token、删除 unused_tokens、qid 唯一完整、mcq/multi/tf、tf A/B、multi 排序去重、total 加和和预算校验。Mock 仅工程测试，不生成正式候选，不改无关 dirty changes。 + +V-A1 独立运行完整命令，核验表头、summary、无 unused_tokens、tf A/B、qid/multi、UTF-8、大小、Token 等式、fail closed、无 API/network。 + +R-A1 只修 findings 和回归测试，不恢复旧契约或手改答案；新 V-A1 全量复验。 + +## 6. A2：Qwen provider/network/Mock/Token gate + +E-A2 在 A1 V PASS 后实现唯一 transport、provider/model/endpoint allowlist、formal Qwen-only gate、显式 Mock、network deny、统一 Token ledger。无 key、错误 provider/model/endpoint、deny 失败、usage 缺失/未知或超预算时正式 fail closed，不能 fallback Mock。本阶段不调用真实 API。 + +V-A2 在隔离环境执行 deny preflight/verify、gate 正反例、显式 Mock、无 key、usage 缺失、ledger/预算测试，并检查未列 transport、fallback 和直接网络。 + +R-A2 只修 findings，不放宽 allowlist、Mock、usage 或调用网络；新 V-A2 全量复验。 + +## 7. A3：离线预处理和 Evidence provenance + +E-A3 在 A2 V PASS 后冻结 PDF/HTML/TXT 离线解析器、版本和配置，允许 OCR/PDF/版面/表格/阅读顺序/结构化转换；逐页记录空页、乱码、正文/表格失败和 parse_warning；Evidence 至少保留 domain、doc_id、page、source_path、char_start、char_end、section、title、table_id、row_id、column_ids、unit、footnote、parent_evidence_id、neighbor_evidence_ids、parse_warning；evidence_id 由版本、来源、位置和内容 hash 确定性生成；不得非 Qwen 摘要/FAQ/结论。 + +V-A3 独立核对页码、字符偏移、表头、完整表格行、单位、脚注、否定、例外、条件边界和 warning，用 synthetic fixture 验字段和 ID 稳定，真实 A 只验 provenance。 + +R-A3 只修 provenance、解析和 fixture，不删页引用、不压边界、不引摘要；新 V-A3 全量复验。 + +## 8. A4：Evidence、Decimal、选项验证、exact-only locator + +E-A4 在 A3 V PASS 后组织完整 Evidence context，保留原文、标题、章节、完整相关表格行/表头、单位、脚注、否定、例外、限定词、条件边界、parent/neighbor 和 Evidence ID。Decimal+白名单结构化操作，禁止 eval/exec；每个计算输入绑定 Evidence ID；选项逐项输出 supported/refuted/insufficient，比较数字、年份、单位、否定、例外、限定词和来源，错误或 insufficient 不得伪造答案。 + +建立 deterministic exact-only backend：允许 doc_id、标题/章节/条款、精确词/短语、数字、年份、日期、百分比、货币、评级、表格字段和原文位置；全部匹配按原始 doc/page/char 顺序稳定返回 Evidence IDs。SQLite/FTS5 如使用只能做无相关性评分 exact token/phrase lookup。禁止 BM25、RRF、vector、dense、hybrid、query-dependent ranking、非 Qwen 语义过滤、语义截断、同义词扩展、问题改写、option 过滤、embedding、transformers、torch、SentenceEncoder、vector DB、rerank。A 严格限制题目非空 doc_ids；无官方 B 仅 synthetic B。 + +V-A4 独立运行 Decimal、无 eval、Evidence 保真、三态选项、exact-only 稳定性和 A scope 测试，构造越界 doc、同义词、排序诱导、空匹配、表格数字、否定/例外边界，确认 fail closed。 + +R-A4 只修 findings，不得以召回为由引入 embedding/BM25/rerank/同义词/语义过滤或删除 Evidence 边界；新 V-A4 全量复验。 + +## 9. A5：Qwen 正式模式和本地候选 + +A5 入口为 A4 V PASS。收到用户精确 `CONFIRM_QWEN_CANDIDATE` 前只能 Mock/dry-run，network actual-deny，不得真实 API/Qwen、不得生成会被误认的正式 answer.csv、不得上传/提交；dry-run 必须 synthetic/mock 标记。授权必须明确 provider、endpoint、实际 model id、opaque credential_ref、egress 和本地输出路径；授权后创建新的 versioned A5 contract/Packet，不复用 dry-run contract。 + +A 分支：只处理官方 A100，每题保留非空 doc_ids，query scope 是题目 doc_ids 严格交集，使用 A 时间窗口；不得删除 doc_ids、改题或伪造 B。 + +B 分支：只有官方 B100 实际存在、可审计 A 有效成绩已提供、B 时间窗口已到、用户另发 `CONFIRM_QWEN_CANDIDATE-B` 且 B 专属 locator/scope 就绪时才可启用。B 无 doc_ids。当前无官方 B，不得生成/上传/声称真实 B；synthetic B 只能非赛事 dry-run。 + +E-A5 授权正式模式执行 Qwen；规划、压缩、证据判断、生成、自检等所有模型调用进入同一 ledger，<=5,000,000；检索仍 exact-only；不引入 embedding/rerank/非 Qwen 过滤。A 分支生成本地官方 A100 answer.csv；B 分支只有满足 B 前置后生成官方 B100。保存 raw call、ledger、Evidence/Decision Artifact、metadata、candidate hashes、preflight；不上传/提交/报 accuracy。 + +V-A5 独立重算 contract/Packet/Prompt/Manifest hashes，验证 Qwen-only、model、endpoint、egress、credential、A/B split、doc_ids、时间/授权、Evidence、ledger、预算、CSV/preflight，确认非 Mock/非 Qwen。V 不修改 candidate、ledger、raw call、Evidence、Decision Artifact、metadata 或 CSV。 + +R-A5 不得就地修改 candidate、ledger、raw call、Evidence、Decision Artifact、metadata、CSV 或 hash，不得手改答案/绕过 validator/preflight。R 触及代码、Prompt、config、dependency、input、ledger、generator、validator 或 preflight 实质内容时，旧 candidate 作废并保留旧 hash；创建新的 A5 R stage-contract/Packet，按机械回退链生成全新 candidate，新 V-A5 全量复验。 + +## 10. A6：独立只读最终验收 + +A6 只在 A0-A5 V PASS 后启动；A6 只读，不调用 Qwen/API/网络，不生成/修改/重生成 candidate。 + +至少运行： + +```text +PYTHONDONTWRITEBYTECODE=1 .venv/bin/python -m pytest -p no:cacheprovider +git diff --check +bash -n scripts/test.sh +PYTHONDONTWRITEBYTECODE=1 .venv/bin/python -m compileall -q awareliquid submit.py examples +``` + +compileall 不得直接运行在 dirty working tree。必须先创建 `/tmp/awareliquid-verification//compile-root` 受控只读副本，记录源文件 hashes、快照清单 hash、副本 hash；compileall cwd、argv、read roots、writes、artifacts 全部指向副本或其 TMPDIR；pyc/报告只能写 TMPDIR；working tree 前后 hash 必须完全不变。 + +E-A6 只读收集 A0-A5 contracts、Packets、Prompts、Manifests、sidecars、reports、规则、dirty baseline、测试结果和 candidate，重算 digest，核对内嵌 Packet 与外部 Packet 一致，检查全部 gates、allowlist、symlink、依赖闭包、无 embedding/rerank、A/B、Evidence、Decimal、选项、Qwen gate、ledger、CSV、preflight、candidate immutable、无上传/提交。 + +V-A6 新独立 V 从原始 contract 全量复验，运行规定命令，为每个 AC 提供证据。A5 未授权/dry-run 时可建议 `LOCAL_ENGINEERING_READY`；真实候选合规时可建议 candidate PASS，但不能代替总体 Sol。实质 finding FAIL，旧 candidate 作废并走回退链。 + +R-A6 只能在允许的审计目录补正验收记录/证据，不得改 contract、AC、fail、scope、model、commands、allowlist、项目、candidate、测试。实质问题回最早阶段。 + +## 11. 机械回退链 + +若 A5/A6 发现 candidate、代码、Prompt、config、dependency、input、ledger、generator、validator、preflight 或其他实质问题： + +1. 旧 candidate、ledger、raw calls、Evidence、Decision Artifact、metadata、CSV 和 hashes 原样只读保留并标为失效证据； +2. 创建新的 R stage-contract/Packet，使用新的 run_id 和全新 hashes； +3. 新 R 只处理 finding，不改 AC/fail/scope； +4. 新 V 全量复验； +5. 按依赖顺序重跑所有下游 V； +6. 回到 A5 重新取得授权并由新 A5 E 生成新 candidate； +7. 新 A5 V 全量复验； +8. 新 A6 全量 V； +9. 新 FINAL-PROJECT-V-SOL； +10. 缺授权、model id、credential、网络隔离、官方 B 或 A 有效成绩时 STOP/BLOCKED，不得用 Mock、旧 candidate 或猜测替代。 + +## 12. FINAL-PROJECT-V-SOL + +A6 V PASS 后，创建 Sol contract 前完成 Sol availability probe；Sol 缺失 STOP，不回退。 + +新的独立 `gpt-5.6-sol` 必须从 A0-A6 所有原始 stage-contract、Packet、Prompt、Manifest、sidecars、E/V/R/A6 reports、dirty baseline、rules/input/artifact hashes 全量重读并交叉验收，不能只看 A6 或 candidate。 + +验收： + +- hash domain、sidecar、内嵌 Packet； +- contract immutability； +- model probe； +- E→V→R→V 独立性； +- command isolation/allowlist/network； +- A0-A5 原始语义； +- A/B 数据、时间、授权； +- 禁止 embedding/rerank； +- Evidence、Decimal、选项； +- Qwen-only； +- Token ledger/预算； +- CSV/preflight； +- candidate/ledger/raw call/Evidence/Decision Artifact/metadata/CSV immutable； +- 所有 P0/P1 已关闭； +- dirty worktree 未被改写； +- 未上传/提交。 + +Sol 不调用 API、不重生成、不修改文件。 + +无真实授权候选时，全量 PASS 只能输出 `LOCAL_ENGINEERING_READY`，不得报告 accuracy。 + +有新 A5 真实候选时，只有该 Sol 全量 PASS 才能唯一输出 `QWEN_CANDIDATE_READY`。 + +`A_SUBMISSION_READY` 只有用户另行明确授权上传/提交后才可产生；Controller 不自动上传、提交或消耗机会。 + +## 13. 当前执行纪律 + +- 不要把 gpt-5.6-sol 用作 E/V/R； +- 不要并行派遣写同一 working tree 的 Agent； +- 不要跳过 V 或 R； +- 不要将 Mock、GPT 子 Agent 或非 Qwen 结果当赛事结果； +- 不要调用 Qwen，除非用户明确授权且 A5 门已满足； +- 不要自动上传、提交或生成 A_SUBMISSION_READY； +- 不要绕过 CONFIRM_IMPLEMENTATION； +- 任何不确定性都必须 STOP/BLOCKED，而不是猜测。 + +现在先把本文件设置为 Goal，读取并遵守本文件；未收到 CONFIRM_IMPLEMENTATION 前停在启动门，后续严格按本文件完整执行。"} irresponsive? Need final via tool. We got invalid arguments maybe due prompt too long or target invalid. Could be parameter `thinking` not supported? It is. But error likely because project environment local cannot specify environment local? schema target project with `environment: {type:"local"}` yes. Maybe prompt too long exceeding tool limit. We can create thread with short prompt that tells read file, after we write file. Great! We just wrote file. Retry create_thread with short prompt. Need no new file changes. Use same `codex_app.create_thread`. diff --git a/docs/AFAC2026_OFFICIAL_RULES_AND_EXECUTION_ALIGNMENT_2026-07-19.md b/docs/AFAC2026_OFFICIAL_RULES_AND_EXECUTION_ALIGNMENT_2026-07-19.md new file mode 100644 index 0000000..6083719 --- /dev/null +++ b/docs/AFAC2026_OFFICIAL_RULES_AND_EXECUTION_ALIGNMENT_2026-07-19.md @@ -0,0 +1,355 @@ +# AFAC2026 官方规则与执行对齐 + +> 文档日期：2026-07-19 +> 官方规则来源：[天池赛事页面](https://tianchi.aliyun.com/competition/entrance/532486/information) +> 项目规则原文：[AFAC2026_OFFICIAL_RULES.md](../reference/AFAC2026_OFFICIAL_RULES.md) + +## 1. 文档目的 + +本文件将最新官网规则转换为项目执行约束，作为后续 Prompt、实现、测试、Qwen 评测和提交前检查的工作依据。 + +官网规则优先级高于此前生成的 baseline、advanced、handoff、M0 manifest 和旧 Prompt。旧文档中与本文件冲突的内容必须以本文件为准。 + +## 2. 项目最终目标 + +构建一个金融长文本 Agent，在不修改基座模型参数的前提下，通过本地文档预处理、确定性证据组织和 Qwen API 推理，实现： + +```text +金融长文档 +→ 文档解析与结构化 +→ 题目和候选文档分析 +→ 关键词/章节/数字/条款确定性定位 +→ Evidence 组织与上下文管理 +→ Qwen 证据判断与答案生成 +→ 答案格式校验与 Token 统计 +→ answer.csv +``` + +项目最终优化目标按优先级排列： + +1. 提高 A/B 榜准确率。 +2. 在准确率不下降的情况下减少 Token 消耗。 +3. 严格生成可评测的 `answer.csv`。 +4. 保留 `evidence.json`、处理数据、代码和日志，支持 B 榜前 15 名代码审核。 + +液态神经网络、Embedding、Rerank 和其他新模型不属于当前正式答题主链路。 + +## 3. 赛事规则冻结 + +### 3.1 数据和题型 + +- 全量基准题：200 道。 +- A 榜：100 道，题目提供 `doc_ids`。 +- B 榜：100 道，题目不提供 `doc_ids`，系统必须先定位候选文档。 +- A 榜有效后才有资格进入 B 榜评测。 +- 领域包括：`insurance`、`regulatory`、`financial_contracts`、`financial_reports`、`research`。 +- 题型包括：`mcq`、`multi`、`tf`。 + +题目字段： + +```json +{ + "qid": "fin_a_001", + "domain": "financial_reports", + "split": "A", + "question": "...", + "options": {"A": "...", "B": "...", "C": "...", "D": "..."}, + "answer_format": "multi", + "type": "...", + "doc_ids": ["...", "..."] +} +``` + +文档元数据至少包括 `doc_id`、`path`、`title`。系统必须能够根据 `doc_id` 找到对应原始 PDF 或处理后的文档。 + +### 3.2 模型使用限制 + +正式推理问答阶段只能使用 Qwen 系列模型 API。官网当前指定的基准模型为 `Qwen3.6-plus`。 + +允许在数据预处理和文档解析阶段使用非 Qwen 工具，包括： + +- OCR； +- PDF 文本抽取； +- 版面分析； +- 表格恢复； +- 阅读顺序还原； +- PDF 转结构化文本。 + +正式答题阶段禁止： + +- Embedding 模型； +- 向量检索； +- Embedding 生成的排序或召回结果； +- 非 Qwen Rerank； +- 非 Qwen 候选过滤； +- 非 Qwen 答案投票或纠错； +- 非 Qwen 生成的语义摘要、FAQ、结论或知识库直接参与正式答题。 + +合规的正式路径是： + +```text +允许的文档解析 +→ 确定性关键词/章节/数字/条款/表格索引 +→ 必要的规则检索与证据定位 +→ Qwen API 进行证据判断和答案生成 +``` + +### 3.3 开发测试与正式提交的边界 + +开发阶段可以使用以下工具进行工程验证： + +- `MockChatClient`：验证调用链、解析、格式和错误处理； +- 本地模型：验证 Prompt 和 Evidence 组织； +- GPT Codex 子代理：执行代码实现、只读审计和测试； +- synthetic fixture：验证确定性检索、计算器和输出契约。 + +这些工具不能用于生成正式提交答案，也不能据此声称获得真实 A/B 准确率或赛事成绩。 + +正式提交答案必须由 Qwen API 生成。正式模式缺少 Qwen API、使用非 Qwen 模型或使用 Mock 时必须失败，不得静默回退。 + +## 4. 提交文件契约 + +正式文件名为 `answer.csv`，大小不得超过 100MB。 + +官网示例对应的 CSV 结构为： + +```csv +qid,answer,prompt_tokens,completion_tokens,total_tokens +summary,,3627557,629,3628186 +ins_a_001,B,37201,1,37202 +ins_a_002,A,33063,1,33064 +``` + +冻结要求： + +- 第一行是表头； +- 第二行是 `summary`； +- `summary` 行的 `answer` 为空； +- 不存在 `unused_tokens` 字段； +- 后续每行对应一道题； +- `total_tokens = prompt_tokens + completion_tokens`； +- `summary.total_tokens` 是本次所有模型 API 调用的总和； +- Token 统计覆盖检索摘要、上下文压缩、证据判断、答案生成、自检等所有模型调用； +- 单选题提交一个大写字母； +- 判断题按照题目选项提交 `A` 或 `B`； +- 多选题提交按字母顺序排列、无分隔符的答案，例如 `AC`； +- 多选题漏选、错选、多选均不得分； +- 空答案、非法字符和顺序错误不得提交。 + +此前的无表头格式、`T/F` 判断题格式和 `unused_tokens` 设计均已废弃。 + +## 5. 评测指标 + +设： + +```text +TokenBudget = 5,000,000 +TotalTokens = summary.total_tokens +Accuracy = Correct / Total +TokenScore = max(0, min(1, (TokenBudget - TotalTokens) / TokenBudget)) +FinalScore = 100 * Accuracy * (0.7 + 0.3 * TokenScore) +``` + +排序规则： + +1. 准确率更高者优先； +2. 准确率相同时，`TotalTokens` 更低者优先； +3. 仍相同时，提交时间更早者优先。 + +官网给出的历史基线： + +| 组别 | 正确/样本 | 准确率 | Token 消耗 | +| --- | ---: | ---: | ---: | +| A 组 | 49/100 | 49.0% | 2,991,883 | +| B 组 | 13/100 | 13.0% | 3,884,045 | +| 总计 | 30/200 | 15.0% | 6,875,928 | + +基线说明：仅将长文档输入通用模型不能稳定解决任务，主要提升方向是文档解析、确定性检索、证据聚合、答案约束和领域策略。 + +## 6. 当前数据状态 + +当前项目本地已确认： + +- A 榜题目 100 道； +- A 榜题目包含非空 `doc_ids`； +- 五个领域的题目和原始文档已在 `data/` 中； +- 题库没有标准答案字段； +- 没有独立的答案 key； +- 当前没有官方 B 榜题目文件。 + +因此本地无法计算完整真实 A/B 准确率。可以生成 Qwen 答案并提交平台，由平台使用标准答案评分。 + +当前日期为 2026-07-19，官网时间安排为： + +- A 榜评测：2026-06-08 10:00 至 2026-07-21 20:00； +- B 榜评测：2026-07-22 00:00 至 2026-07-24 17:00。 + +当前优先级必须是 A 榜正式提交准备，不能等待本地 B 榜答案或标准答案。 + +## 7. 代码审核材料 + +B 榜排名前 15 名需要准备以下 `submission.zip`： + +```text +submission.zip +├── answer.csv +├── evidence.json +├── processed_data/ +├── agent/ +├── script/ +├── logs/ +├── requirements.txt +└── README.md +``` + +压缩包总大小不得超过 1GB，并且应支持按照 README 一键运行并生成结果文件。 + +## 8. 现在的推进顺序 + +### 阶段 0：停止旧契约执行 + +当前旧执行 thread 不能继续进入 M1-M6，原因是它使用了已经过时的约束： + +- 判断题 `T/F`； +- 无表头 CSV； +- `unused_tokens`； +- 禁止 Qwen API 的本地离线主链路； +- Mock 作为主要运行后端； +- 旧版 summary 解释。 + +先基于本文件重新生成执行 Prompt，并更新 manifest 的规则来源。 + +### 阶段 1：修正输入和输出契约 + +必须先修改并测试： + +- `awareliquid/adapter/afac_contract.py`； +- `awareliquid/adapter/contracts.py`； +- `awareliquid/adapter/schemas.py`； +- `submit.py`； +- 相关契约测试。 + +验收内容： + +- 表头正确； +- summary 行正确； +- 无 `unused_tokens`； +- `mcq`、`multi` 和 `tf` 输出合法； +- 判断题按照题目 A/B 选项处理； +- 每题 Token 三列一致； +- summary Token 覆盖全部模型调用； +- Mock 不能进入正式模式。 + +### 阶段 2：完成允许的文档预处理 + +从 PDF、HTML、TXT 生成可复现的处理数据，保留： + +- `doc_id`； +- 页码； +- 标题和章节； +- 段落位置； +- 条款编号； +- 表格标题、表头、行、列、单位和脚注； +- 原始文本； +- 解析警告和源文件哈希。 + +预处理可以使用非 Qwen 工具，但不得生成会在正式答题中直接替代 Qwen 推理的语义结论。 + +### 阶段 3：建立正式检索和证据链路 + +使用确定性方法完成： + +- 领域和文档过滤； +- 关键词和短语匹配； +- 章节和条款编号定位； +- 数字、年份、日期、百分比和货币定位； +- 表格行列定位； +- 选项级证据收集； +- 多文档证据合并。 + +禁止 Embedding、向量召回和非 Qwen 语义 Rerank。 + +### 阶段 4：接入 Qwen 正式推理 + +正式答案链路使用 Qwen API： + +```text +题目 +→ 确定性候选证据 +→ Qwen 证据判断 +→ Qwen 答案生成 +→ 本地格式校验 +→ Token 账本 +``` + +每个 API 调用都必须记录真实的 `prompt_tokens`、`completion_tokens` 和 `total_tokens`。 + +### 阶段 5：生成 A 榜候选文件 + +在正式提交前必须完成本地预检： + +- 题目数量和 `qid` 完整； +- 没有重复 `qid`； +- 每题答案符合题型； +- 判断题只输出 A/B； +- 多选答案排序、去重、无分隔符； +- summary 行完整； +- `summary.total_tokens` 等于所有模型调用总和； +- Token 不超过 5,000,000； +- 文件有表头； +- CSV 小于 100MB； +- UTF-8 编码； +- 无额外调试输出； +- 使用真实 Qwen 结果，不是 Mock 或 GPT 子代理结果。 + +### 阶段 6：使用剩余 3 次提交机会 + +提交策略： + +1. 第一次提交：完整 Qwen A 榜候选，确认格式和链路有效； +2. 第二次提交：仅在第一次结果暴露明确问题后进行针对性修复； +3. 第三次提交：保留给最终稳定版本。 + +提交前不得使用 Mock 结果替代 Qwen 结果，也不得把 GPT 子代理结果写入正式 `answer.csv`。 + +### 阶段 7：B 榜开放后 + +B 榜开放后： + +- 读取真实 B 榜题目； +- 不通过删除 A 榜 `doc_ids` 伪造 B 榜； +- 使用确定性候选文档定位； +- 仍然只用 Qwen API 进行正式推理； +- 生成 B 榜 `answer.csv`； +- 保存 `evidence.json` 和完整复现材料。 + +## 9. 当前代码必须修正的已知问题 + +当前代码和旧 manifest 仍包含以下过时实现： + +- `unused_tokens` 参数和相关测试； +- 无表头 summary-first CSV； +- 判断题正式输出 `T/F`； +- 默认无 API Key 时回退 Mock； +- `hybrid` embedding 检索路径； +- README 中与正式规则不一致的输出描述； +- 旧 M0 manifest 将 PDF 页图的冲突解释为不可继续执行。 + +这些内容在更新 Prompt 后必须由独立执行 Agent 修改，再由独立验收 Agent 重跑测试。 + +## 10. 最终完成标志 + +项目只有在以下条件同时满足时，才可以进入正式 A 榜提交： + +- 官方输入契约已按本文件冻结； +- CSV 契约已按官网示例实现； +- 判断题输出 A/B； +- 没有 `unused_tokens`； +- 正式模式无法使用 Mock 或非 Qwen 模型； +- 正式路径不使用 Embedding 和 Rerank； +- A 榜 100 道题全部生成答案； +- 所有 API Token 都被准确统计； +- `answer.csv` 通过本地预检并小于 100MB； +- 本地保存了提交版本、配置、日志和文件哈希。 + +最终目标是获得平台真实评分，不是把 Mock、GPT 子代理或 synthetic fixture 的结果称为比赛准确率。 diff --git a/docs/AFAC_M0_CONTRACT_EVIDENCE.md b/docs/AFAC_M0_CONTRACT_EVIDENCE.md new file mode 100644 index 0000000..6c3c3ee --- /dev/null +++ b/docs/AFAC_M0_CONTRACT_EVIDENCE.md @@ -0,0 +1,146 @@ +# AFAC M0 Contract Evidence + +Run: `afac-20260718-203623-019f74b8` +Source thread: `019f74b8-0bbf-74f0-a168-1ab34711a671` + +This record freezes only the local offline input, internal, and formal-output +contracts. It does not establish real A/B accuracy, real token usage, ranking, +or submission readiness. + +## Source precedence and decisions + +1. **Primary source:** `downloads/AFAC-赛题四-PDF/金融长文本 Agent 的动态记忆压缩与高效问答.pdf`, page 4 for the A/B distinction and page 6 for the input/output image. The page-level evidence supplied for this run states that A provides `doc_ids`, B locates documents itself, inputs include `qid/domain/question/options/answer_format/type/doc_ids`, the generic options description is A/B/C/D, and the formal first row is `summary,5000000,1234567,0` without a header. It also states single-choice is one letter, multi-choice is sorted concatenated letters, judgment is `T/F`, and Token accounting covers every model-interface call. The page does not define how the fourth summary value is computed; the literal example is arithmetically inconsistent with `budget-used`, so that meaning remains unresolved. +2. **Local extraction of the primary source:** `output/pdf/AFAC-赛题四-Markdown/金融长文本 Agent 的动态记忆压缩与高效问答-精简版.md:188-224` records the fields, option keys, summary row, answer formats, Token scope, and A/B distinction. Its `:136-143` view is the compact table used for the line references in the earlier verification report. +3. **Lower-priority reference:** `reference/advanced.md:561-575` (also rendered in the Baseline PDF Markdown at `output/pdf/AFAC-赛题四-Markdown/定稿版【跑通Baseline】赛题四：金融长文本Agent的动态记忆压缩与高效问答挑战.md:561-575`) shows a header and a five-field prompt/completion/total summary. This conflicts with the primary page-6 image. The primary source wins; the conflict is retained in the M0 manifest and the implementation emits headerless summary-first rows. + +## Frozen input contract + +`awareliquid.adapter.afac_contract.parse_question` and +`awareliquid.adapter.contracts.validate_question` validate the explicit +`split` field and never infer it from `qid`, `type`, or `doc_ids`. + +| split | `doc_ids` missing | `doc_ids: null` | `doc_ids: []` | non-empty list | +| --- | --- | --- | --- | --- | +| A | fail `DOC_IDS_REQUIRED_FOR_A` | fail `DOC_IDS_REQUIRED_FOR_A` | fail `EMPTY_DOC_IDS` | accepted and preserved in order | +| B | accepted and normalized to `None` | accepted and normalized to `None` | fail `EMPTY_DOC_IDS` | fail `SPLIT_DOC_IDS_CONFLICT` | + +Split values are explicit `A`/`B` (case-normalized); both entry points accept +explicit lowercase `a`/`b` and normalize the internal standard object to +uppercase before applying the split-specific `doc_ids` rules. An absent, +non-string, or otherwise invalid split is a structured error. `mcq` and `multi` options must be an object with exactly +the four keys `A`, `B`, `C`, `D`, with string values. The verified real A set +uses two-key `A`/`B` options for its 20 `tf` questions; the synthetic fixture +also exercises a four-key TF object, so both TF shapes are accepted. The +formal TF output remains strictly `T` or `F`. `answer_format` is one of `mcq`, +`multi`, or `tf`; `type` is retained only as a hint. + +### R-M0-R5 split-normalization repair + +The two M0 validation entry points now have the same explicit split boundary: +they accept only string `A`/`B` values case-insensitively, normalize accepted +values to uppercase, and then apply the existing four-state `doc_ids` table. +The paired regression in `tests/test_contracts_m0.py` covers lowercase `a` and +`b` through both entry points. It confirms normalization without permitting +split inference or weakening the A/B `doc_ids`, TF option, formal `T`/`F`, or +explicit `unused_tokens` fail-closed contracts. + +## Internal standard object + +`ParsedQuestion` contains `qid`, `domain`, normalized `split`, `question`, the +keyed options (A/B for audited real `tf` questions, or A/B/C/D for `mcq` and +`multi`; four-key `tf` remains accepted for compatibility), `answer_format`, `type_hint`, and `doc_ids` as +`Optional[Tuple[str, ...]]`. `to_dict()` is deterministic and preserves A-list +document order. `SubmissionAnswer` validates formal answer letters and +internally consistent per-question token counts. `SubmissionSummary` accepts +explicit `budget_tokens`, `used_tokens`, and `unused_tokens`; it never derives +the fourth summary value from the first two. + +## Frozen formal output + +`render_submission_csv` emits CSV with no header only for local structural +tests. It refuses to render with `MISSING_UNUSED_TOKENS` until the caller +supplies `unused_tokens` explicitly, and serializes that supplied value without +derivation. `submit.py` likewise requires explicit `--budget-tokens`, +`--used-tokens`, and `--unused-tokens` before it can write an output file. This +is a structural contract only, not an interpretation of the fourth field. + +The official meaning of that field is not frozen because page 6 labels it +`未用` but gives `summary,5000000,1234567,0`; therefore this stage must not +claim that `unused = budget - used` is authoritative. The formal-output +contract remains **unresolved** at M0 and +`official_submission_allowed=false`. + +```text +summary,5000000,, +,,,, +``` + +`mcq` accepts one of A/B/C/D; `multi` accepts a non-empty sorted, de-duplicated +letter string such as `BC`; `tf` accepts exactly `T` or `F`. Summary `used` +must cover all per-question `total_tokens` and cannot exceed the supplied +budget. Token fields represent all model-interface calls represented by the +answer rows; this local implementation reports only supplied/mock counts. No +official submission is emitted while the fourth summary field is unresolved. + +## Blocking gates for later stages + +These gates are recorded at M0 and remain blocking requirements; none is +evidence that M1 or M3 has been started or passed. + +1. The page-6 official summary example is the highest-priority source for the + fourth summary column. Its calculation rule must be confirmed from official + material before an official submission can be authorized; the explicit API + only preserves structure and does not resolve that rule. +2. M1 must freeze the actual PDF body-parser version and configuration. + `pdfinfo` alone is not evidence of body quality. Each page with empty text, + garbling, or table-extraction failure must retain the original page reference + and a `parse_warning`. +3. M3 may claim 100% required coverage only for synthetic golden fixtures. + Real A validation must instead preserve and verify lossless + locator/hash/source/page/character metadata. +4. Any B validation must use an independent synthetic fixture. It must not + delete or simulate away the real A `doc_ids` inputs. +5. Before the first verification run, confirm `.venv` and development + dependencies. Verification must use `PYTHONDONTWRITEBYTECODE=1 pytest -p + no:cacheprovider`; caches, SQLite state, and reports belong only under + `/tmp/awareliquid-verification/`, not the workspace. +6. Step 7 remains offline: network, external API or key use, GPU, embedding, + and reranking are prohibited. + +## Data status and fixture boundary + +The local filesystem audit found `data/` with 578 readable files: five +`data/questions/group_a/*.json` files (20 questions each, 100 total), all with +explicit `split=A` and non-empty `doc_ids`, plus 573 raw files. Raw counts by +top-level domain are financial_contracts=14, financial_reports=10, insurance=16, +regulatory=513 (including attachments/HTML/TXT), and research=20. No +`data/questions/group_b/` directory or B question set was found. The resulting +status is `REAL_A_DATA_FOUND_B_NOT_VERIFIED`. This permits local component +verification against A inputs only; it does not permit real A/B accuracy or +leaderboard claims. The five A question file SHA-256 values are recorded in the +stage manifest. Shape audit: mcq/multi A/B/C/D=80, tf A/B=20. `downloads/` remains source/tutorial material and `examples/` +contains sample data only. The fixtures +`tests/fixtures/afac_m0_synthetic_golden.json` and +`tests/fixtures/afac_synthetic_golden/questions.json` are explicitly marked +synthetic and are for local component verification only. + +### PDF encryption re-audit (R-M0-R4) + +An independent offline `pdfinfo` audit traversed every case-insensitive +`data/raw/**/*.[pP][dD][fF]` candidate. It parses the value from lines beginning +exactly `Encrypted:` and classifies values beginning `yes` as encrypted, because +`pdfinfo` includes permissions and algorithm details after that value. The +reproducible result is 190 PDFs, 190 readable PDFs, 0 `pdfinfo` errors, 188 +unencrypted PDFs, 2 encrypted PDFs, and 11,667 pages. + +The encrypted paths are `data/raw/insurance/1.pdf` (21 pages, RC4) and +`data/raw/insurance/4.pdf` (33 pages, AES). Their fields are respectively +`Encrypted: yes (print:yes copy:no change:no addNotes:no algorithm:RC4)` and +`Encrypted: yes (print:yes copy:no change:no addNotes:no algorithm:AES)`. + +This conflicts with the prior user claim that there were zero encrypted PDFs; +the directly reproducible local evidence takes precedence and keeps M0 failed. +It also corrects V-M0-R3's reported count of nine. That report does not retain +the prior audit script, so its mechanism cannot be established; the correction +source is R-M0-R4's direct parsing of the `Encrypted:` fields above, rather than +an assertion about the unavailable script. diff --git a/docs/AFAC_QWEN_FINANCIAL_QA_RESEARCH_AND_IMPLEMENTATION_HANDOFF_2026-07-18.md b/docs/AFAC_QWEN_FINANCIAL_QA_RESEARCH_AND_IMPLEMENTATION_HANDOFF_2026-07-18.md new file mode 100644 index 0000000..250cbd6 --- /dev/null +++ b/docs/AFAC_QWEN_FINANCIAL_QA_RESEARCH_AND_IMPLEMENTATION_HANDOFF_2026-07-18.md @@ -0,0 +1,248 @@ +# AFAC Qwen 金融长文本 QA：研究与实现交接文档 + +> 文档版本：2026-07-18 +> 适用范围：AFAC「金融长文本 Agent 的动态记忆压缩与高效问答」赛事方案 +> 交接目标：让下一位开发者可以在当前仓库上继续实现合规、高准确率、可验证的 Qwen API 答题系统。 + +## 0. 一页结论 + +本项目的正式比赛主线已经冻结为： + +```text +问题分类 +→ 规则/结构化词法检索 +→ 证据覆盖与缺口补检 +→ 保留完整证据窗口、表格行、脚注和来源 +→ 本地安全计算/候选选项验证 +→ Qwen 生成最终答案 +→ 格式、数值和证据一致性校验 +``` + +核心判断：比赛的主要瓶颈不是训练新的液态神经网络，而是让冻结的 Qwen 能够稳定拿到正确、完整、位置合理的金融证据。 + +液态神经网络、Embedding、Rerank、RAPTOR、GraphRAG 等暂不进入正式答题主链路。液态神经网络只保留为后续研究方向；Embedding/Rerank 及微调方案在当前赛事约束下不作为实现前提。 + +## 1. 事实来源与规则边界 + +优先级从高到低： + +1. [赛事精简规则](<../output/pdf/AFAC-赛题四-Markdown/金融长文本 Agent 的动态记忆压缩与高效问答-精简版.md>) +2. [baseline 解析](../reference/baseline.md) +3. [advanced 解析](../reference/advanced.md) +4. 当前仓库代码和测试 + +已确认的赛事约束： + +| 事项 | 结论 | +|---|---| +| 基座模型 | 仅使用 Qwen 系列，通过官方 API 调用 | +| 微调 | 不做微调 | +| reasoning | 不开启 Qwen reasoning/思考模式；不依赖显式 CoT 输出 | +| Embedding | 禁止作为正式方案依赖 | +| Rerank | 禁止作为正式方案依赖 | +| Token 预算 | 全部接口调用总和不超过 500 万 Token | +| 评分 | 准确率权重 70%，Token 效率权重 30% | +| A 榜 | 提供 `doc_ids`，可以离线深度预处理 | +| B 榜 | 不提供 `doc_ids`，需要自行定位候选文档 | +| 本地预处理 | 不消耗 API Token，应尽量在离线阶段完成 | + +当前阶段的默认模型策略：先以赛事材料中使用的 `qwen-plus` 作为基准，优先优化证据链路、问题路由和验证器；在这些模块稳定前，不切换模型作为主要实验变量。 + +## 2. 当前仓库状态 + +### 2.1 可以复用的部分 + +- [`awareliquid/memory/knowledge_store.py`](../awareliquid/memory/knowledge_store.py)：SQLite 存储和 FTS5/BM25 词法检索，可以作为正式方案的基础。 +- [`awareliquid/adapter/qwen_client.py`](../awareliquid/adapter/qwen_client.py)：冻结 Qwen API 适配和 Token 用量统计。 +- [`awareliquid/adapter/qa_agent.py`](../awareliquid/adapter/qa_agent.py)：已经具备 `doc_ids` 过滤、选项输入和基础答题编排。 +- [`README.md`](../README.md) 与 [`docs/ARCHITECTURE.md`](./ARCHITECTURE.md)：明确了外部记忆适配器和 Qwen API 的整体边界。 + +### 2.2 正式比赛前必须调整的部分 + +1. [`awareliquid/memory/encoder.py`](../awareliquid/memory/encoder.py) 默认使用 `intfloat/multilingual-e5-small`，正式比赛路线必须关闭或隔离 dense embedding 路径。 +2. [`awareliquid/adapter/qa_agent.py`](../awareliquid/adapter/qa_agent.py) 当前主要是 dense + BM25 的 RRF 混合和单轮检索，需要改成词法/结构化检索和多阶段证据补检。 +3. [`awareliquid/adapter/compressor.py`](../awareliquid/adapter/compressor.py) 采用句子级有损压缩，可能删除“除外、但是、截至、同比口径、脚注”等决定答案的条件。正式方案应改为证据窗口、完整表格行和来源保留。 +4. 当前缺少金融表格对象、指标/年份/单位解析、受限计算器、候选答案验证器和证据充分性判定。 + +### 2.3 当前系统的真实定位 + +当前仓库是一个可工作的研究原型，不是已经完全符合赛事规则的最终提交系统。下一位开发者不应先做模型训练，而应先完成“合规词法检索 + 结构化证据 + 可验证答题”的闭环。 + +## 3. 论文依据与可吸收机制 + +时间均为 arXiv 首次提交时间；可靠性用于区分“已通过会议/期刊评审”和“最新但尚未充分验证”的工作，不代表论文指标可以直接迁移到本赛事。 + +### 3.1 主线论文 + +| 优先级 | 论文 | 时间与状态 | 应吸收的机制 | 适配度 | +|---|---|---|---|---| +| P0 | [FinQA](https://arxiv.org/abs/2109.00122) | 2021-09-01；EMNLP 2021 | 金融数值推理、可解释计算程序、文本与表格联合证据 | 高 | +| P0 | [Fine-grained Retriever for FinQA](https://arxiv.org/abs/2206.08506) | 2022-06-17；FinQA 竞赛系统论文 | 不只取整段文本，还要定位相关行和单元格 | 高 | +| P0 | [Chain-of-Table](https://arxiv.org/abs/2401.04398) | 2024-01-09；ICLR 2024 | 让表格作为中间状态逐步演化，而不是用自然语言硬算 | 高 | +| P0 | [ECoRAG](https://arxiv.org/abs/2506.05167) | 2025-06-05；有公开代码的预印本 | 根据证据充分性进行压缩，不足时继续检索 | 高 | +| P0 | [RI²VER](https://arxiv.org/abs/2506.00425) | 2025-05-31；ACL 2025 Findings | 为每个候选答案独立收集并验证跨段证据 | 高 | +| P0 | [FinLongDocQA / FinLongDocAgent](https://arxiv.org/abs/2604.03664) | 2026-04-04；最新预印本 | 长财报、多表、跨表、多轮检索、中间计算和结果验证 | 极高，但需自行验证 | +| P0 | [PAR²-RAG](https://arxiv.org/abs/2603.29085) | 2026-03-30；最新预印本 | 先做高召回 Coverage，再做有证据充分性控制的 Commitment | 高，但需自行验证 | +| P1 | [Program of Thoughts](https://arxiv.org/abs/2211.12588) | 2022-11-22；TMLR 2023 | 将计算交给外部程序，降低模型心算错误 | 高 | +| P1 | [Adaptive-RAG](https://arxiv.org/abs/2403.14403) | 2024-03-21；NAACL 2024 | 根据问题复杂度选择无检索、单轮检索或多轮检索 | 高 | +| P1 | [Question Decomposition for RAG](https://arxiv.org/abs/2507.00355) | 2025-07-01；ACL SRW 2025 | 将多跳问题拆成子问题分别取证 | 高；去掉论文中的 rerank | +| P1 | [Never Lost in the Middle](https://arxiv.org/abs/2311.09198) | 2023-11-15；ACL 2024 | 训练结论不能直接使用，但可借鉴证据分解和上下文组织 | 中高 | +| P1 | [Lost in the Middle, and In-Between](https://arxiv.org/abs/2412.10079) | 2024-12-13；预印本 | 多跳证据之间的距离也会影响模型使用效果 | 中高 | + +### 3.2 研究和评测参考 + +- [BRIGHT](https://arxiv.org/abs/2407.12883)：2024-07-16 首次提交，2025-03-26 更新。它说明需要推理才能找到相关文档的检索任务很难，适合借鉴评测方法，不是直接实现方案。 +- [ReAct](https://arxiv.org/abs/2210.03629)：2022-10-06，ICLR 2023。可借鉴工具调用循环，但正式方案应改成确定性的有限状态机，不能依赖自由形式 reasoning。 +- [RAPTOR](https://arxiv.org/abs/2401.18059)：2024-01-31，ICLR 2024。创新是层次化摘要树，但依赖 Embedding、聚类和摘要，和正式规则冲突。 +- [GraphRAG](https://arxiv.org/abs/2404.16130)：2024-04-24，预印本。可借鉴文档引用关系，但不建议当前阶段构建 LLM 生成的知识图谱和社区摘要。 +- [M3DocRAG](https://arxiv.org/abs/2411.04952)：2024-11-07，预印本。适合图表/视觉证据研究，但多模态检索器不进入当前正式主链路。 +- [STEC](https://arxiv.org/abs/2607.10795)：2026-07-12，刚提交的预印本。提出“按候选答案聚合证据再验证”，创新性强，但太新，只作为实验候选。 +- [Agentic Financial RAG](https://arxiv.org/abs/2605.05409)：2026-05-06，但已撤回，作者指出实验方法存在根本性错误，不采信其指标。 + +## 4. 冻结后的目标架构 + +### 4.1 离线阶段：零 API Token + +对每个 PDF/文档建立以下结构： + +- `doc_id` +- 页码、章节、标题、段落位置 +- 条款编号和法条/章节引用 +- 表格标题、表头、行名、列名、单位、脚注 +- 财务指标、年份、日期、百分比、货币、数值归一化形式 +- 原始文本和邻近文本窗口 +- 文档内引用关系和相邻证据关系 + +建立的索引只使用合规的本地方法： + +- FTS5/BM25 +- 标题/章节索引 +- 数字、年份、日期、单位索引 +- 正则匹配 +- 表格行列索引 +- 页码和邻近窗口索引 + +### 4.2 在线阶段：按问题复杂度路由 + +问题先进入确定性路由器： + +| 类型 | 处理方式 | +|---|---| +| 事实题 | 关键词、章节、精确匹配、邻近证据窗口 | +| 条款/例外题 | 条款编号、否定词、例外词、完整上下文 | +| 公式/计算题 | 指标和年份定位，交给本地受限计算器 | +| 比较题 | 每个目标分别取证，再进行统一比较 | +| 多跳题 | 先高召回，再根据证据缺口补检 | +| 选择题 | 对每个选项分别收集支持/反驳证据 | + +### 4.3 证据循环 + +```text +第一轮：问题/选项/目标词法召回 +→ 检查是否覆盖每个文档、年份、指标和表格 +→ 补充章节、相邻段、完整表格行、脚注和例外条件 +→ 判断证据是否足够 +→ 不足则针对缺口继续检索 +→ 组装少量但完整的证据块 +→ 调用 Qwen +→ 校验答案、选项、数字和格式 +``` + +上下文组装原则： + +- 原始证据优先于摘要； +- 证据块内保留前后文； +- 关键证据放在上下文开头或结尾； +- 相关的多个证据尽量相邻； +- 表格问题尽量保留完整相关行/列，而不是切成孤立句子； +- 每块证据保留 `doc_id/page/section/table/row` 来源信息。 + +## 5. 实施路线图 + +### P0：先完成正式合规闭环 + +1. 增加正式配置，关闭 dense embedding 和所有 rerank 入口。 +2. 保留并强化 FTS5/BM25，加入标题、章节、数字、年份和单位索引。 +3. 设计统一的 `Evidence` 数据结构，必须携带原文和来源位置。 +4. 重写压缩器为“证据窗口组装器”，避免无依据删除句子。 +5. 增加问题类型路由器。 +6. 增加金融表格和数值解析。 +7. 增加安全的本地计算器：只允许白名单运算，不执行 Qwen 任意代码。 +8. 增加选项级支持/反驳证据验证。 +9. 增加 Token 统计、停止条件和最终格式校验。 + +### P1：提高复杂题准确率 + +1. 实现 PAR²-RAG 风格的“覆盖 → 深挖”两阶段检索。 +2. 实现 ECoRAG 风格的证据充分性检查。 +3. 对比较题和多跳题实现逐文档提取、再统一比较。 +4. A 榜利用已知 `doc_ids` 做深度离线预处理。 +5. B 榜增加候选文档定位阶段，但继续使用词法和结构方法。 + +### P2：评测和可选研究 + +1. 建立按题型拆分的离线评测集。 +2. 对比单轮检索、多轮检索、证据窗口和候选验证的增益。 +3. 评估 `STEC` 的候选答案证据聚合思想。 +4. 只有在正式主链路稳定后，才研究液态神经网络或其他小模型。 + +## 6. 必须做的消融实验 + +至少保留以下实验组： + +| 实验组 | 检索 | 结构化证据 | 多轮补检 | 本地计算 | 选项验证 | +|---|---|---|---|---|---| +| Baseline | 当前实现 | 否 | 否 | 否 | 否 | +| E1 | BM25/FTS5 | 否 | 否 | 否 | 否 | +| E2 | BM25/FTS5 | 是 | 否 | 否 | 否 | +| E3 | BM25/FTS5 | 是 | 是 | 否 | 否 | +| E4 | BM25/FTS5 | 是 | 是 | 是 | 否 | +| E5 | BM25/FTS5 | 是 | 是 | 是 | 是 | + +每组至少记录： + +- 总准确率； +- 事实题、计算题、比较题、多跳题准确率； +- 表格数值执行准确率； +- 证据覆盖率； +- 单题及总 Token； +- 错误类型：漏检、错表、单位错、年份错、例外条件丢失、计算错、选项判断错。 + +## 7. 验收标准 + +正式提交前应满足： + +- 默认运行路径不加载 Embedding 模型； +- 默认运行路径不调用 Rerank 模型； +- 默认不启用 Qwen reasoning/思考模式； +- 每个答案都能追溯到证据块和来源位置； +- 计算题由本地白名单计算器复核； +- 选择题经过候选选项级验证； +- A 榜严格使用给定 `doc_ids`； +- B 榜具备独立的候选文档定位流程； +- API Token 统计准确且可复现； +- 输出格式可以直接生成赛事要求的提交文件。 + +## 8. 明确不做的事情 + +- 不把液态神经网络作为当前比赛主链路； +- 不训练本地 Embedding/Rerank 模型； +- 不使用 RAPTOR 的 Embedding + 聚类 + 摘要树作为正式实现； +- 不使用 GraphRAG 的 LLM 知识图谱/社区摘要作为第一阶段方案； +- 不让 Qwen 直接执行任意 Python 代码； +- 不为了节省 Token 删除未经验证的金融原文、脚注、单位和例外条件； +- 不在证据链路未稳定前通过更换 Qwen 模型掩盖检索问题。 + +## 9. 下一位开发者的第一步 + +按以下顺序开始，不要从液态神经网络训练开始： + +1. 运行当前测试，记录当前 Baseline。 +2. 增加“正式合规模式”，关闭 dense embedding。 +3. 实现 `Evidence` 结构和原文窗口组装。 +4. 实现题型路由和 BM25/FTS5 结构化召回。 +5. 加入表格/数值解析与本地安全计算。 +6. 加入选项级验证。 +7. 最后实现多轮证据补检和证据充分性停止条件。 + +完成上述 P0 后，再讨论是否需要引入任何新的模型或液态神经网络实验。 diff --git a/docs/PREREGISTRATION.md b/docs/PREREGISTRATION.md new file mode 100644 index 0000000..56bd855 --- /dev/null +++ b/docs/PREREGISTRATION.md @@ -0,0 +1,48 @@ +# 预注册制度（Pre-registration） + +借鉴 M1 仓库（MT-LNN）的纪律：**判定规则写死于代码、先于结果落盘**。 +目的：防止结果出来后再"反向定制"判优标准（结果驱动的规则改动会让任何 +结论都不可信）。 + +## 规则 + +1. **先落盘，后运行**：每次对照实验（新策略 vs 现状、A vs B）在跑之前， + 判定规则必须先写进实验脚本的 docstring 或本文件附录——精确到比较 + 指标、方向和阈值。先提交（commit message 用 `prereg:` 前缀），再跑。 +2. **判负也归档**：判负/无效结果同样写入 `docs/RESULTS.md` 的 + SUPERSEDED / 筛查区（留痕不删除），不得静默丢弃。 +3. **引用门槛**：对外引用对照结论前必须过 + `benchmarks/experiment_protocol.py::publishable`（≥3 seeds、非双峰、 + 配对 p<0.05）。过不了门的结果只能入档，不能引用。 +4. **不做清单**：证据已闭环的问题不得无新证据重开（见下）。 +5. **提交动词纪律**（M1 五段式，按需取用）： + `prereg:`（规则先落盘）→ `bench:`（运行脚本/口径）→ `results:` + （结果，含判负）→ `docs:`（RESULTS.md 归档）。 + +## 不做清单（证据已闭环，无新证据不得重开） + +- 检索/编码路径**不得回退到内建 `hash()`**：盐随进程随机，跨进程不可 + 复现（2026-09-06 bench 数字漂移事故的根因；修复见 + `benchmarks/bench_adapter.py` 与 `tests/conftest.py`）。 +- 单次运行 bench 计数**不得写进对外材料**：只作筛查/smoke；要引用就扩 + 多 seed / 多题集并过 `publishable()` 门（`docs/RESULTS.md` 证据政策）。 +- `.sha256` sidecar **必须是磁盘字节的哈希**（`raw_hash(path)`），不得 + 用 `object_hash`（canonical compact JSON）代替：落盘是 pretty-print + 形式，两者必然失配（2026-09-06 校验器首跑抓到 66 处，全部验证等价后 + 重写；管线出处 `scripts/bootstrap_a1_terra_r1.py` 的 `sidecar()` 调用 + 点混用两种哈希）。 + +## 预注册模板 + +```markdown +## [实验名] — YYYY-MM-DD + +- 动机：一句话（预期证伪/证实什么）。 +- 两臂：arm-a = <配置>；arm-b = <配置>（除被测变量外全同）。 +- 预注册判据（跑之前写死）： + - 指标：<如 recall@4 / retention / token 用量> + - 判优规则：<如 arm-a 各题 >= arm-b 且配对符号检验 p<0.05 判 a 胜> + - 判负处置：<判负后写入 RESULTS.md 筛查区，不做 X> +- 运行计划：seeds/题集/环境；结果归档位置（log/json 路径约定）。 +- 结果（跑完填）：数字 + publishable() 输出 + verdict。 +``` diff --git a/docs/RESULTS.md b/docs/RESULTS.md new file mode 100644 index 0000000..27835ba --- /dev/null +++ b/docs/RESULTS.md @@ -0,0 +1,109 @@ +# Canonical Evidence Base（单一事实源） + +Last reconciled: **2026-09-07** + +本文件是本仓库实验证据的**唯一事实源**。任何其他文档（README、HANDOFF、 +docs/runs/、幻灯片、对外表述）与本文件冲突时，**以本文件为准**——错的 +是那份文档，需要改的是它。 + +## 证据政策 + +1. 每个对外引用的数字必须指回本文件 Proven 表，并给出仓库内可复现产物 + 路径（反引号包裹，如 `benchmarks/results/bench_adapter_fake_20260906_r1.log`）。 + 引用路径的真实性由 `scripts/check_results_refs.py` 机器校验（CI 门禁， + 见 `.github/workflows/ci.yml`）。 +2. **引用门槛（E0 协议门）**：凡对外引用的**对照数字**（A vs B、新旧 + 策略对比），必须过 `benchmarks/experiment_protocol.py` 的 + `publishable()` 判定——≥3 seeds、双臂非双峰、配对检验 p<0.05。 + **单次运行的计数（如本仓库 bench 的 N/N 题）属于筛查/smoke**：只能 + 用于回归对照和方向判断，不得作为对外结论引用，直到扩展为多 seed / + 多题集并过门。 +3. **撤回/取代不删除**：过时或被推翻的数字移入下面的 SUPERSEDED / + 筛查区表，保留原始数值、日期和原因。 +4. 判定规则先于结果落盘：见 `docs/PREREGISTRATION.md`。 + +## PROVEN（当前有效） + +| Claim | Number | Status | 证据 | +|---|---|---|---| +| bench lexical stand-in（8 题，哈希确定性修复后，单次筛查运行）：hybrid recall@4 | 8/8 (100%)，dense 7/8 | 筛查有效 | `benchmarks/results/bench_adapter_fake_20260906_r1.log` | +| 同上：answer retention（hybrid / dense） | 8/8 / 7/8 | 筛查有效 | `benchmarks/results/bench_adapter_fake_20260906_r1.log` | +| 同上：prompt context tokens vs 全文档 | 1254 vs 22668（−94%） | 筛查有效 | `benchmarks/results/bench_adapter_fake_20260906_r1.log` | +| 同上：正式格式有效行 / 判定 | 8/8，RESULT: PASS | 筛查有效 | `benchmarks/results/bench_adapter_fake_20260906_r1.log` | +| bench `--fake` 跨进程可复现（修复内建 `hash()` 后） | 连跑 3 次输出逐字节一致 | 已验证 | `benchmarks/results/bench_adapter_fake_20260906_r1.log`、`benchmarks/results/bench_adapter_fake_20260906_r2.log`、`benchmarks/results/bench_adapter_fake_20260906_r3.log` | +| 单元测试基线（离线全量，197 个测试） | 197 passed | 当前有效 | `benchmarks/results/pytest_20260907.log`（运行 `scripts/test.sh`） | +| M0 输入/内部/正式输出合同冻结（仅合同，非准确率） | 冻结，来源优先级与冲突已留痕 | 已归档 | `docs/AFAC_M0_CONTRACT_EVIDENCE.md` | +| run 证据链完整性（18/19 个 run 目录有 manifest + sha256 sidecar；a0-20260719T001816-26628 为杂散目录，仅 baseline.txt 无 manifest，校验器显式 SKIP 列出） | 可机器校验，当前 PASS | 已归档 | `docs/runs/`（`scripts/check_results_refs.py` 输出 PASS） | + +## SUPERSEDED / 筛查区（留痕不删除） + +| Claim | Number | 处置 | 证据 / 备注 | +|---|---|---|---| +| bench e5（真实 multilingual embedder，6 题集）：recall@4、retention | 6/6、6/6；压缩率 0.48 | **历史单次筛查，未复核**——重跑需下载模型，离线环境无法复核；引用前必须重跑并过 E0 门 | `README.md` Validation 节（旧 6 题集） | +| bench lexical stand-in 旧 6 题集：recall@4、retention、压缩率 | 5/6、5/6、0.46 | **SUPERSEDED (2026-09-06)**——被 8 题集确定性运行取代（题集与编码器哈希均已变更，数字不可比） | 旧 `benchmarks/bench_adapter.py` 运行，无留档日志 | +| 修复前 bench `--fake` 两次运行 8/8 与 7/8 互相矛盾 | — | **RETRACTED (2026-09-06)**——根因：`benchmarks/bench_adapter.py` 的 `LexicalEncoder` 用内建 `hash()`，盐随进程随机；两值均为随机盐的运气，不代表系统能力 | 无留档（修复前输出未保存），仅本行记录；修复后日志见 `benchmarks/results/` | +| run 目录 JSON sidecar 哈希（提交于 f459eca） | 66 处失配 | **CORRECTED (2026-09-06)**——sidecar 哈希的是 canonical compact JSON，落盘是 pretty-print 版，内容语义相同（逐一验证 canonical 哈希吻合后重写为磁盘字节哈希，0 处遗留失配）；管线出处：`scripts/bootstrap_a1_terra_r1.py` 的 `sidecar()` 调用点混用 `object_hash` 与 `raw_hash` | `docs/runs/`（修复后 `scripts/check_results_refs.py` PASS） | + +## What we do NOT claim（负面声明） + +1. **端到端答案准确率未测量**：最终字母由冻结模型选择，离线无法评估； + 需要 live Qwen key 才能测（README Scope 节同款声明）。 +2. **91.7% 无本仓库产物支撑**：外部表述（含 M1 仓库 README 对本仓库的 + 描述）中的 ~91.7% eval score 在本仓库找不到任何可复现证据，在证据 + 链补齐前不得引用。 +3. **`answer.csv` 不是可提交状态**：token 三列全 0 是占位，不代表真实 + 用量或排名。 +4. **M0 合同证据不建立**真实 A/B 准确率、真实 token 用量、排名或提交 + 就绪（`docs/AFAC_M0_CONTRACT_EVIDENCE.md` 开头的自限声明）。 +5. **bench 表格数字不是结论**：8 题单次运行只证明"管线工作、门槛条件 + 满足"，不证明检索/压缩质量优于任何基线——那需要多 seed / 多题集 + + `publishable()` 过门。 + +## 对账日志 + +- **2026-09-06**（首次对账）： + - 移植 M1 仓库 E0 协议门为 `benchmarks/experiment_protocol.py`（零依赖， + 7 个契约测试：`tests/test_experiment_protocol.py`）。 + - 修复两处内建 `hash()` 跨进程不确定性：`tests/conftest.py` 的 + `FakeEncoder`、`benchmarks/bench_adapter.py` 的 `LexicalEncoder`； + bench 三连跑逐字节一致（见 PROVEN 表）。 + - 收敛 run 证据清单：`docs/runs/` 19 个目录全部过 + `scripts/check_results_refs.py`（manifest 路径 + sha256 sidecar）。 + - 确立本文件为单一事实源；README bench 表与本文件对齐（见下）。 + - **事故 1（bench 数字漂移）**：`bench --fake` 连续两次运行结果矛盾， + 根因为 `LexicalEncoder` 内建 `hash()` 盐随机 → 修复后三连跑逐字节 + 一致，旧数字（8/8、7/8）撤回。新增 bench 确定性回归测试 + （`tests/test_bench_determinism.py`，PYTHONHASHSEED=1/2 双盐对照， + 断言两次 stdout 逐字节一致），锁住 2026-09-06 事故，替代人工三连跑。 + - **事故 2（sidecar 失配）**：`scripts/check_results_refs.py` 首跑抓到 + 66 处 JSON sidecar 失配——管线哈希 canonical compact JSON 而落盘是 + pretty-print 版；逐一验证对象等价后重写为磁盘字节哈希，纪律入 + `docs/PREREGISTRATION.md` 不做清单。 +- **2026-09-07（评审轮）**：只读评审发现并当日修复—— + (a) PROVEN 表"19/19 run 目录"声称超出实际（实为 18/19，杂散目录 + a0-20260719T001816-26628 无 manifest），校验器改为对无 manifest 的 + run 目录显式 SKIP 列出，不再静默跳过； + (b) `bimodality_1d` 对量化分数误报双峰（簇内全为重复值时类内散度为 + 0，任意两个相邻取值的 gap_ratio 爆炸，如 6/8 与 7/8 的正常一致性 + 数据会被判双峰），增加绝对间隙下限 0.25（按 [0,1] 量纲设计）； + (c) 含 None 的臂会让 `publishable()` 路径 TypeError 崩溃（seeds / + 双峰 / 符号检验三层各崩一处），统一收敛为 `_as_floats` 清洗—— + 脏臂由 seeds 门槛判负，`publishable()` 任何路径不再崩溃； + (d) 确定性测试增加"bench 输出 == 留档日志"逐字节断言，把 README / + 本文件引用的数字钉死到产物（改口径必须显式重新留档并对账）； + (e) pytest 结果留档为 `benchmarks/results/pytest_20260907.log`， + Proven 表不再有无产物支撑的数字； + (f) README Validation 表撤下 e5 历史数字本体（单次运行计数不入对外 + 材料，见 `docs/PREREGISTRATION.md`），保留指向本文件的指针； + (g) CI 加最小权限（`permissions: contents: read`）与 + `timeout-minutes: 30`。 +- **2026-09-07（遗留 NIT 修复轮）**：评审遗留三项全部修复—— + (a) md5 分桶逻辑收敛为共享模块 `awareliquid/hashing.py::stable_hash` + （`tests/conftest.py` 与 `benchmarks/bench_adapter.py` 不再各写一份； + 行为逐位不变，日志钉扎测试确认输出仍与留档一致）； + (b) 校验器对**歧义裸文件名**从"静默取第一个命中"改为 FAIL 并列出全部 + 候选（`scripts/check_results_refs.py`，新增对应测试）； + (c) 5 个 `scripts/bootstrap_*.py` 的 sidecar 调用点全部改为写盘后的 + 磁盘字节哈希（`raw_hash`/`file_digest`）——换 RUN_ID 重跑不再复制 + 66 处失配事故；manifest/contract 内部 `*_sha256` 字段保持 canonical + 对象哈希语义（有文档化的 hash domain，与 sidecar 是两套含义）。 diff --git a/docs/plans/2026-07-18-afac-compliant-qa.md b/docs/plans/2026-07-18-afac-compliant-qa.md new file mode 100644 index 0000000..cc76328 --- /dev/null +++ b/docs/plans/2026-07-18-afac-compliant-qa.md @@ -0,0 +1,64 @@ +# AFAC 合规金融问答系统实施计划 + +> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. + +**Goal:** 在不使用正式路径 Embedding/Rerank/微调/任意代码执行的前提下，完成 AwareLiquid 的可追溯词法检索、结构化证据、问题路由、本地金融计算、候选答案验证、多轮证据补检和可重复评测。 + +**Architecture:** 离线阶段使用 SQLite/FTS5/BM25 与结构化索引保存文档、表格和来源；在线阶段由确定性路由器决定检索计划，经有限轮次的证据覆盖与缺口补检组装原文窗口，再调用 Qwen 生成答案并进行本地格式、数值和证据校验。A 榜严格限定给定 `doc_ids`，B 榜先做候选文档定位。 + +**Tech Stack:** Python 3.10+, SQLite FTS5/BM25, pytest, Qwen OpenAI-compatible API, 标准库正则/JSON/算术白名单。 + +--- + +## 执行门禁 + +每个 Step 依次创建独立的 `E-Step-N` 执行 Agent 与 `V-Step-N` 只读验收 Agent。只有验收 `PASS` 才进入下一步；`FAIL` 必须由新的 `R-Step-N` 修复 Agent 处理，再创建新的验收 Agent。每个 Step 只触碰自身范围，并记录测试、风险和报告。 + +## Step 0：基线和规则合规审计 + +- 阅读交接、规则、README、架构和核心代码，建立可重复 baseline。 +- 审计 dense embedding、rerank、`doc_ids`、Qwen token 统计和压缩丢失风险。 +- 仅添加审计/合规测试，不实现后续功能。 +- 通过独立验收后进入 Step 1。 + +## Step 1：合规词法/结构化检索 + +- 强化 FTS5/BM25、标题/章节/页码/段落/表格标题、数字/年份/日期/单位索引。 +- 在数据库查询层严格应用 `doc_ids`；区分 A 榜给定文档检索与 B 榜候选文档定位。 +- 增加中英文金融 fixture 和反例测试，正式模式不加载 dense/rerank。 + +## Step 2：证据对象和上下文组装 + +- 引入带 `doc_id/page/section/source_type/text/table_id/row_id/column_ids/score` 的统一 Evidence。 +- 保留原文、表头、单位、脚注、条款例外和邻近上下文，使用首尾优先和证据相邻布局控制预算。 + +## Step 3：问题类型路由 + +- 用规则支持 fact/clause/formula/comparison/multihop/mcq，输出稳定可序列化计划。 +- 为计算、逐文档比较、多跳补检和选项验证选择安全路径；路由不调用 API，并提供回退。 + +## Step 4：金融表格和本地安全计算 + +- 解析表格标题/表头/行列/单位/脚注，按指标和年份定位单元格。 +- 仅允许白名单加减乘除、百分比、同比、差值、比较，并保留输入证据和计算过程。 +- 明确拒绝任意代码、除零、单位冲突和缺失数据。 + +## Step 5：候选答案验证 + +- 对每个选项独立召回支持/反驳证据，核对数字、年份、单位和限定词。 +- 输出 `supported/refuted/insufficient` 及证据 ID、数值检查和置信度，拒绝表面词汇匹配。 + +## Step 6：多轮证据补检和充分性 + +- 实现有限状态的 Coverage → Gap → Commitment 流程，记录每轮查询、命中、证据缺口和 token。 +- 具备最大轮数/单题 token 上限、去重、无新增停止、跨文档最低覆盖及安全降级。 + +## Step 7：评测、消融和提交集成 + +- 固化 baseline、E1–E5 消融，按题型统计准确率、证据覆盖率、计算/格式正确率及 token。 +- 验证 A/B 榜分离、异常处理和提交文件格式，补齐回归测试与运行说明。 + +## Final：独立总体验收 + +- 只读检查全部 diff、报告、测试和输出；运行全量及关键路径测试。 +- 只有规则合规、功能完整、回归通过且 A/B 榜与 token 账本均可解释时输出 `GO`。 diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md b/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md new file mode 100644 index 0000000..8f56174 --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md @@ -0,0 +1,7 @@ +You are the independent E-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","-"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"7b6f8ba49c31768234fc25c66ac78c5bbd49931db62db3a354a2e62ac729b80d","cache_timestamp":"2026-07-18T19:45:28.943230Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"872a19de8091ea4caa31c7f7a481afea8ea05ea1a3f368c011101d15c6b0ae86","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"a4ec21a9f9a54e5f96db55e4b4d3e76f50f2f50a8859f8aa15611ed714abcd96","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T19:47:06.620679Z","provider_base_url_origin":"https://api.pptoken.cc"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/reference","/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid","/Users/aricredemption/Projects/AwareLiquid-M2/tests","/Users/aricredemption/Projects/AwareLiquid-M2/examples","/Users/aricredemption/Projects/AwareLiquid-M2/docs","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T194706Z-luna-v2","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T194706Z-luna-v2"]} +END PACKET diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md.sha256 b/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md.sha256 new file mode 100644 index 0000000..7748dbf --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md.sha256 @@ -0,0 +1 @@ +ee3691bc3fcba242043396d1e1da4ddf034ced08e22fbac7e7e2ccac9cb14094 prompt.md diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md b/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md new file mode 100644 index 0000000..52f1829 --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md @@ -0,0 +1,7 @@ +You are the independent R-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","-"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"7b6f8ba49c31768234fc25c66ac78c5bbd49931db62db3a354a2e62ac729b80d","cache_timestamp":"2026-07-18T19:45:28.943230Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"872a19de8091ea4caa31c7f7a481afea8ea05ea1a3f368c011101d15c6b0ae86","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"a4ec21a9f9a54e5f96db55e4b4d3e76f50f2f50a8859f8aa15611ed714abcd96","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T19:47:06.620679Z","provider_base_url_origin":"https://api.pptoken.cc"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/reference","/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid","/Users/aricredemption/Projects/AwareLiquid-M2/tests","/Users/aricredemption/Projects/AwareLiquid-M2/examples","/Users/aricredemption/Projects/AwareLiquid-M2/docs","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T194706Z-luna-v2","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T194706Z-luna-v2"]} +END PACKET diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md.sha256 b/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md.sha256 new file mode 100644 index 0000000..dac5071 --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md.sha256 @@ -0,0 +1 @@ +3d6cda7a083cd0c474d04c60f7007616ee96dfbe1e8b2d3261958e157e4cb20b prompt.md diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md b/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md new file mode 100644 index 0000000..054b890 --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md @@ -0,0 +1,7 @@ +You are the independent V-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, 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dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T194706Z-luna-v2"]} +END PACKET diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md.sha256 b/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..a3e586a --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +bdac1c8f0a544dc8ca7053cad8965bce2b9f184db7393b20e71d880eca892365 prompt.md diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/creation-record.json b/docs/runs/a0-20260718T194706Z-luna-v2/creation-record.json new file mode 100644 index 0000000..f45400a --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna-v2/creation-record.json @@ -0,0 +1 @@ +{"contract_sha256":"46f8c728ba15402dd45ebfd0516504833eb2f9b6cee4698b404dc7d458db5e16","created_at":"2026-07-18T20:10:10.173296Z","manifest_sha256":"1da0388c500ce969dd6994f3d650002e99ad6f268777198b808b1d6cec85a050","packet_sha256":"5903c8390458119bb64250c4e7d5dd7e02a3a4293809dd7c1fe6e6fdf948b613","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md","sha256":"ee3691bc3fcba242043396d1e1da4ddf034ced08e22fbac7e7e2ccac9cb14094"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md","sha256":"3d6cda7a083cd0c474d04c60f7007616ee96dfbe1e8b2d3261958e157e4cb20b"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md","sha256":"bdac1c8f0a544dc8ca7053cad8965bce2b9f184db7393b20e71d880eca892365"}},"run_id":"a0-20260718T194706Z-luna-v2"} diff --git a/docs/runs/a0-20260718T194706Z-luna-v2/manifest.json 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+{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/manifest.json","/tmp/awareliquid-verification/a0-20260718T194706Z-luna-v2/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260718T194706Z-luna-v2/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260718T194706Z-luna-v2/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md"],"contract_sha256":"46f8c728ba15402dd45ebfd0516504833eb2f9b6cee4698b404dc7d458db5e16","model":"gpt-5.6-luna","model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","packet_sha256":"5903c8390458119bb64250c4e7d5dd7e02a3a4293809dd7c1fe6e6fdf948b613","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/E-A0/prompt.md","sha256":"ee3691bc3fcba242043396d1e1da4ddf034ced08e22fbac7e7e2ccac9cb14094"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/R-A0/prompt.md","sha256":"3d6cda7a083cd0c474d04c60f7007616ee96dfbe1e8b2d3261958e157e4cb20b"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna-v2/V-A0/prompt.md","sha256":"bdac1c8f0a544dc8ca7053cad8965bce2b9f184db7393b20e71d880eca892365"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260718T194706Z-luna-v2","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","-"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"7b6f8ba49c31768234fc25c66ac78c5bbd49931db62db3a354a2e62ac729b80d","cache_timestamp":"2026-07-18T19:45:28.943230Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"872a19de8091ea4caa31c7f7a481afea8ea05ea1a3f368c011101d15c6b0ae86","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"a4ec21a9f9a54e5f96db55e4b4d3e76f50f2f50a8859f8aa15611ed714abcd96","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T19:47:06.620679Z","provider_base_url_origin":"https://api.pptoken.cc"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/reference","/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid","/Users/aricredemption/Projects/AwareLiquid-M2/tests","/Users/aricredemption/Projects/AwareLiquid-M2/examples","/Users/aricredemption/Projects/AwareLiquid-M2/docs","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T194706Z-luna","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T194706Z-luna"]} +END PACKET + diff --git a/docs/runs/a0-20260718T194706Z-luna/E-A0/prompt.md.sha256 b/docs/runs/a0-20260718T194706Z-luna/E-A0/prompt.md.sha256 new file mode 100644 index 0000000..8d55175 --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna/E-A0/prompt.md.sha256 @@ -0,0 +1 @@ +fb5795000b3ac600879da130dc70f99d975f8b77567cb4deb24ae4f621da391a prompt.md diff --git a/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md b/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md new file mode 100644 index 0000000..910ac4d --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md @@ -0,0 +1,8 @@ +You are the independent R-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","-"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"7b6f8ba49c31768234fc25c66ac78c5bbd49931db62db3a354a2e62ac729b80d","cache_timestamp":"2026-07-18T19:45:28.943230Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"872a19de8091ea4caa31c7f7a481afea8ea05ea1a3f368c011101d15c6b0ae86","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"a4ec21a9f9a54e5f96db55e4b4d3e76f50f2f50a8859f8aa15611ed714abcd96","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T19:47:06.620679Z","provider_base_url_origin":"https://api.pptoken.cc"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/reference","/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid","/Users/aricredemption/Projects/AwareLiquid-M2/tests","/Users/aricredemption/Projects/AwareLiquid-M2/examples","/Users/aricredemption/Projects/AwareLiquid-M2/docs","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T194706Z-luna","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T194706Z-luna"]} +END PACKET + diff --git a/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md.sha256 b/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md.sha256 new file mode 100644 index 0000000..0a258db --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md.sha256 @@ -0,0 +1 @@ +c872aada22d284187bb6c08056b86947c9ee9d780922019d6b1d63869bbabd17 prompt.md diff --git a/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md b/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md new file mode 100644 index 0000000..354562f --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md @@ -0,0 +1,8 @@ +You are the independent V-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, 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dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T194706Z-luna"]} +END PACKET + diff --git a/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md.sha256 b/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..d587ae3 --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +ee542e8dcea396602455dae47efad5c487779189919201a8399e5fede9bb7608 prompt.md diff --git a/docs/runs/a0-20260718T194706Z-luna/creation-record.json b/docs/runs/a0-20260718T194706Z-luna/creation-record.json new file mode 100644 index 0000000..7bf03a3 --- /dev/null +++ b/docs/runs/a0-20260718T194706Z-luna/creation-record.json @@ -0,0 +1 @@ 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+{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/manifest.json","/tmp/awareliquid-verification/a0-20260718T194706Z-luna/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260718T194706Z-luna/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260718T194706Z-luna/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md"],"contract_sha256":"81fe4881dadc89a4d266a0c4bcaf38321c1dc4543d2fa244b5f772af0f2ba440","model":"gpt-5.6-luna","model_hash":"754069492d03ec59819c46b10351ba969cedf2e95a8cadd9791c38b22c36e88a","packet_sha256":"fd7f357112e445373b37a17bae2cf7a4aa7f5428e79886543816a9a3dd5a5623","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/E-A0/prompt.md","sha256":"fb5795000b3ac600879da130dc70f99d975f8b77567cb4deb24ae4f621da391a"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/R-A0/prompt.md","sha256":"c872aada22d284187bb6c08056b86947c9ee9d780922019d6b1d63869bbabd17"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T194706Z-luna/V-A0/prompt.md","sha256":"ee542e8dcea396602455dae47efad5c487779189919201a8399e5fede9bb7608"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260718T194706Z-luna","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":null,"active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"5fdbb81a64fb1940ecaca55548a1195851b8999abdf83ac9b50e63aef0239d05","cache_timestamp":"2026-07-18T20:22:10.515733Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"707b074f9994310a023816c6c6d0d8c1192b163866270e193158825e4d71c2d2","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:22:15.810646Z","provider_base_url_origin":null},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T202002Z-luna-r1","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T202002Z-luna-r1"]} +END PACKET diff --git a/docs/runs/a0-20260718T202002Z-luna-r1/E-A0/prompt.md.sha256 b/docs/runs/a0-20260718T202002Z-luna-r1/E-A0/prompt.md.sha256 new file mode 100644 index 0000000..ebb573d --- /dev/null +++ b/docs/runs/a0-20260718T202002Z-luna-r1/E-A0/prompt.md.sha256 @@ -0,0 +1 @@ +7b8ed8ece6ab18a6caba59b6b0d4e4a29727d01e16a5de37288e01028172b8a6 prompt.md diff --git a/docs/runs/a0-20260718T202002Z-luna-r1/R-A0/prompt.md b/docs/runs/a0-20260718T202002Z-luna-r1/R-A0/prompt.md new file mode 100644 index 0000000..c48f72e --- /dev/null +++ b/docs/runs/a0-20260718T202002Z-luna-r1/R-A0/prompt.md @@ -0,0 +1,7 @@ +You are the independent R-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":null,"active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"5fdbb81a64fb1940ecaca55548a1195851b8999abdf83ac9b50e63aef0239d05","cache_timestamp":"2026-07-18T20:22:10.515733Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"707b074f9994310a023816c6c6d0d8c1192b163866270e193158825e4d71c2d2","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:22:15.810646Z","provider_base_url_origin":null},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T202002Z-luna-r1","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T202002Z-luna-r1"]} +END PACKET diff --git a/docs/runs/a0-20260718T202002Z-luna-r1/R-A0/prompt.md.sha256 b/docs/runs/a0-20260718T202002Z-luna-r1/R-A0/prompt.md.sha256 new file mode 100644 index 0000000..1a9dfff --- /dev/null +++ b/docs/runs/a0-20260718T202002Z-luna-r1/R-A0/prompt.md.sha256 @@ -0,0 +1 @@ +6770866bdbb840f4c6d2e2aa0756d0dcdea848ab74d0d4ce2c31616be4720c90 prompt.md diff --git a/docs/runs/a0-20260718T202002Z-luna-r1/V-A0/prompt.md b/docs/runs/a0-20260718T202002Z-luna-r1/V-A0/prompt.md new file mode 100644 index 0000000..881f4fc --- /dev/null +++ b/docs/runs/a0-20260718T202002Z-luna-r1/V-A0/prompt.md @@ -0,0 +1,7 @@ +You are the independent V-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, 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--- /dev/null +++ b/docs/runs/a0-20260718T202002Z-luna-r1/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +b1089b5d6323414c6f26dd9a15a45a822c274e0970c62e45f646c61671ab1ea2 prompt.md diff --git a/docs/runs/a0-20260718T202002Z-luna-r1/creation-record.json b/docs/runs/a0-20260718T202002Z-luna-r1/creation-record.json new file mode 100644 index 0000000..ae000ea --- /dev/null +++ b/docs/runs/a0-20260718T202002Z-luna-r1/creation-record.json @@ -0,0 +1 @@ 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":null,"active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"ccc2b43943187819c8468f3f137b190b7bda6a6a9207f3a223a339dd05ddf122","cache_timestamp":"2026-07-18T20:22:58.711573Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"707b074f9994310a023816c6c6d0d8c1192b163866270e193158825e4d71c2d2","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:23:03.858581Z","provider_base_url_origin":null},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The previous E/V saw a different .codex config/cache environment than the Controller probe; 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, 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rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The previous E/V saw a different .codex config/cache environment than the Controller probe; this run records a fresh Controller registry probe and does not call the API.","The previous read allowlist omitted repository-root files and dirty roots such as scripts/ and downloads/; this run allows the repository root read-only and keeps writes limited to the verification directory.","The previous V report compared raw file hashes including the final LF; this run verifies canonical JSON hashes with the contract-defined LF exclusion before dispatch."],"previous_run":"a0-20260718T194706Z-luna-v3-subagent","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T202215Z-luna-r2","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T202215Z-luna-r2"]} +END PACKET diff --git a/docs/runs/a0-20260718T202215Z-luna-r2/V-A0/prompt.md.sha256 b/docs/runs/a0-20260718T202215Z-luna-r2/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..f9e87dd --- /dev/null +++ b/docs/runs/a0-20260718T202215Z-luna-r2/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +30bb621a447bc48686b957ed340cd5a3d837a8cb3a2afe832150d3889569271a prompt.md diff --git a/docs/runs/a0-20260718T202215Z-luna-r2/creation-record.json b/docs/runs/a0-20260718T202215Z-luna-r2/creation-record.json new file mode 100644 index 0000000..fd8d8f5 --- /dev/null +++ b/docs/runs/a0-20260718T202215Z-luna-r2/creation-record.json @@ -0,0 +1 @@ +{"contract_sha256":"94605086ff28d9b3c5b06d6a98d4f90bb1e5a84742927d513c068fab5249183d","created_at":"2026-07-18T20:23:03.867923Z","manifest_sha256":"42433bcce65266a604a5de681b8ab00e11515f043d78d51c1e7d230e91c16499","packet_sha256":"420fa4aea6330d8fa80abf535537ce2f6857ae2f83ad0134dd8d048f7554456e","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/E-A0/prompt.md","sha256":"58caa8ffe9d620ce2812f00912eb6ea5eb5f4a65ad22bb9de82cd623d116b05e"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/R-A0/prompt.md","sha256":"0d8518a690182a81b1f63cc41657b20b02071c826e1ee05e425f67d740ab5084"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/V-A0/prompt.md","sha256":"30bb621a447bc48686b957ed340cd5a3d837a8cb3a2afe832150d3889569271a"}},"run_id":"a0-20260718T202215Z-luna-r2"} diff --git a/docs/runs/a0-20260718T202215Z-luna-r2/manifest.json b/docs/runs/a0-20260718T202215Z-luna-r2/manifest.json new file mode 100644 index 0000000..e654623 --- /dev/null +++ b/docs/runs/a0-20260718T202215Z-luna-r2/manifest.json @@ -0,0 +1 @@ +{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/manifest.json","/tmp/awareliquid-verification/a0-20260718T202215Z-luna-r2/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260718T202215Z-luna-r2/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260718T202215Z-luna-r2/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/R-A0/prompt.md"],"contract_sha256":"94605086ff28d9b3c5b06d6a98d4f90bb1e5a84742927d513c068fab5249183d","model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","packet_sha256":"420fa4aea6330d8fa80abf535537ce2f6857ae2f83ad0134dd8d048f7554456e","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/E-A0/prompt.md","sha256":"58caa8ffe9d620ce2812f00912eb6ea5eb5f4a65ad22bb9de82cd623d116b05e"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/R-A0/prompt.md","sha256":"0d8518a690182a81b1f63cc41657b20b02071c826e1ee05e425f67d740ab5084"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202215Z-luna-r2/V-A0/prompt.md","sha256":"30bb621a447bc48686b957ed340cd5a3d837a8cb3a2afe832150d3889569271a"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260718T202215Z-luna-r2","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} diff --git a/docs/runs/a0-20260718T202215Z-luna-r2/manifest.json.sha256 b/docs/runs/a0-20260718T202215Z-luna-r2/manifest.json.sha256 new file mode 100644 index 0000000..6da26c3 --- /dev/null +++ b/docs/runs/a0-20260718T202215Z-luna-r2/manifest.json.sha256 @@ -0,0 +1 @@ +d3dd531ce202932c6628046b60b77cc60b4b59e8a650a25d6ad7770e58141992 manifest.json diff --git a/docs/runs/a0-20260718T202215Z-luna-r2/packet.json b/docs/runs/a0-20260718T202215Z-luna-r2/packet.json new file mode 100644 index 0000000..227dc70 --- /dev/null +++ b/docs/runs/a0-20260718T202215Z-luna-r2/packet.json @@ -0,0 +1 @@ +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks 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Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":null,"active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"cb319f9e35e9617e508ff476034d0ee0d5d0556c8426faefe46b15e8dc2aeaee","cache_timestamp":"2026-07-18T20:28:41.489444Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"707b074f9994310a023816c6c6d0d8c1192b163866270e193158825e4d71c2d2","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:28:45.056333Z","provider_base_url_origin":null},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["A fork_context=true host-consistency probe matched Controller cwd, HEAD, status count, and config hash; 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":null,"active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"cb319f9e35e9617e508ff476034d0ee0d5d0556c8426faefe46b15e8dc2aeaee","cache_timestamp":"2026-07-18T20:28:41.489444Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"707b074f9994310a023816c6c6d0d8c1192b163866270e193158825e4d71c2d2","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:28:45.056333Z","provider_base_url_origin":null},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["A fork_context=true host-consistency probe matched Controller cwd, HEAD, status count, and config hash; 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, 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rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["A fork_context=true host-consistency probe matched Controller cwd, HEAD, status count, and config hash; this run records a fresh dynamic registry probe and does not call the API.","The previous read allowlist omitted repository-root files and dirty roots such as scripts/ and downloads/; this run allows the repository root read-only and keeps writes limited to the verification directory.","The previous V report compared raw file hashes including the final LF; this run verifies canonical JSON hashes with the contract-defined LF exclusion before dispatch."],"previous_run":"a0-20260718T202215Z-luna-r2","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T202828Z-luna-r3","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T202828Z-luna-r3"]} +END PACKET diff --git a/docs/runs/a0-20260718T202828Z-luna-r3/V-A0/prompt.md.sha256 b/docs/runs/a0-20260718T202828Z-luna-r3/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..049fea6 --- /dev/null +++ b/docs/runs/a0-20260718T202828Z-luna-r3/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +bf6cf77fbf6036a6acbfe46aced8f00ef67c23ff0bc46384716cd6ba7cd422fb prompt.md diff --git a/docs/runs/a0-20260718T202828Z-luna-r3/creation-record.json b/docs/runs/a0-20260718T202828Z-luna-r3/creation-record.json new file mode 100644 index 0000000..c09efb0 --- /dev/null +++ b/docs/runs/a0-20260718T202828Z-luna-r3/creation-record.json @@ -0,0 +1 @@ +{"contract_sha256":"d6a178342359480f531f395b17727ccb56c937c578b632c46d57a9bce2c000cf","created_at":"2026-07-18T20:28:45.061225Z","manifest_sha256":"16ac1d1e54c8baaa7d582f62384d2d61a7474d4cf221f1d0e9c558d5e795465d","packet_sha256":"231183ce491951064fc58ab61834cda3e61ad66aebe8cbc1383bc917d10daf02","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/E-A0/prompt.md","sha256":"9dfa582a8ac6a3abf1a9a9161f5335bf4c65a3c2402fe8107ab3e69650b391ec"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/R-A0/prompt.md","sha256":"06d170516bd07745c2086d213ef08b0d5511239469ce7e73f1dcb3ad6457e726"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/V-A0/prompt.md","sha256":"bf6cf77fbf6036a6acbfe46aced8f00ef67c23ff0bc46384716cd6ba7cd422fb"}},"run_id":"a0-20260718T202828Z-luna-r3"} diff --git a/docs/runs/a0-20260718T202828Z-luna-r3/manifest.json 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+{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/manifest.json","/tmp/awareliquid-verification/a0-20260718T202828Z-luna-r3/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260718T202828Z-luna-r3/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260718T202828Z-luna-r3/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/R-A0/prompt.md"],"contract_sha256":"d6a178342359480f531f395b17727ccb56c937c578b632c46d57a9bce2c000cf","model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","packet_sha256":"231183ce491951064fc58ab61834cda3e61ad66aebe8cbc1383bc917d10daf02","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/E-A0/prompt.md","sha256":"9dfa582a8ac6a3abf1a9a9161f5335bf4c65a3c2402fe8107ab3e69650b391ec"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/R-A0/prompt.md","sha256":"06d170516bd07745c2086d213ef08b0d5511239469ce7e73f1dcb3ad6457e726"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T202828Z-luna-r3/V-A0/prompt.md","sha256":"bf6cf77fbf6036a6acbfe46aced8f00ef67c23ff0bc46384716cd6ba7cd422fb"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260718T202828Z-luna-r3","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any 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b/docs/runs/a0-20260718T203619Z-luna-r4/stage-contract.json.sha256 @@ -0,0 +1 @@ +8732e92344ed95edefd52c3dc9d0d4079b5aa2a49b40086beedb4ba8c0482972 stage-contract.json diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/E-A0/prompt.md b/docs/runs/a0-20260718T203619Z-luna-r5/E-A0/prompt.md new file mode 100644 index 0000000..65a13fb --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/E-A0/prompt.md @@ -0,0 +1,8 @@ +You are the independent E-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. 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Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point ascending","separators":[",",":"],"sidecar_final_lf":true},"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"8c18d1ba6724b7d799a03b8ed62eca7125aaad3682e5d6f6ad3c6b35767ebe10","cache_timestamp":"2026-07-18T20:43:58.761720Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"98daaf260e087c093a8b46aaf6ee16bcc74c49f46d8caa657fe53155251f378b","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:44:01.740965Z","provider_base_url_origin":null},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r5/model-probe.json","sha256":"ef8de9125c757c196849b8f78f78071460188c8719610295db00e010f72ba740"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous baseline relied on Git untracked enumeration and omitted four downloads PDFs; this run hashes every regular file and symlink under the repository except .git, so ignored and directory-folded files are included.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF."],"previous_run":"a0-20260718T203619Z-luna-r4","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T203619Z-luna-r5","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T203619Z-luna-r5"]} +END PACKET diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/E-A0/prompt.md.sha256 b/docs/runs/a0-20260718T203619Z-luna-r5/E-A0/prompt.md.sha256 new file mode 100644 index 0000000..6596272 --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/E-A0/prompt.md.sha256 @@ -0,0 +1 @@ +72715297ad6b836a6554d215ff93f2dbccc6797c2b9ac0a36c9c8ad79f02d456 prompt.md diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/R-A0/prompt.md b/docs/runs/a0-20260718T203619Z-luna-r5/R-A0/prompt.md new file mode 100644 index 0000000..ec75631 --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/R-A0/prompt.md @@ -0,0 +1,8 @@ +You are the independent R-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point ascending","separators":[",",":"],"sidecar_final_lf":true},"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"8c18d1ba6724b7d799a03b8ed62eca7125aaad3682e5d6f6ad3c6b35767ebe10","cache_timestamp":"2026-07-18T20:43:58.761720Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"98daaf260e087c093a8b46aaf6ee16bcc74c49f46d8caa657fe53155251f378b","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:44:01.740965Z","provider_base_url_origin":null},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r5/model-probe.json","sha256":"ef8de9125c757c196849b8f78f78071460188c8719610295db00e010f72ba740"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous baseline relied on Git untracked enumeration and omitted four downloads PDFs; this run hashes every regular file and symlink under the repository except .git, so ignored and directory-folded files are included.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF."],"previous_run":"a0-20260718T203619Z-luna-r4","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T203619Z-luna-r5","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T203619Z-luna-r5"]} +END PACKET diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/R-A0/prompt.md.sha256 b/docs/runs/a0-20260718T203619Z-luna-r5/R-A0/prompt.md.sha256 new file mode 100644 index 0000000..371f35f --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/R-A0/prompt.md.sha256 @@ -0,0 +1 @@ +727fb890b94e1d5d8d925891fbff4a4c5ce925a94354f152a8cb10fb3ecfa4bd prompt.md diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/V-A0/prompt.md b/docs/runs/a0-20260718T203619Z-luna-r5/V-A0/prompt.md new file mode 100644 index 0000000..0930678 --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/V-A0/prompt.md @@ -0,0 +1,8 @@ +You are the independent V-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point ascending","separators":[",",":"],"sidecar_final_lf":true},"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"8c18d1ba6724b7d799a03b8ed62eca7125aaad3682e5d6f6ad3c6b35767ebe10","cache_timestamp":"2026-07-18T20:43:58.761720Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"98daaf260e087c093a8b46aaf6ee16bcc74c49f46d8caa657fe53155251f378b","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:44:01.740965Z","provider_base_url_origin":null},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r5/model-probe.json","sha256":"ef8de9125c757c196849b8f78f78071460188c8719610295db00e010f72ba740"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous baseline relied on Git untracked enumeration and omitted four downloads PDFs; this run hashes every regular file and symlink under the repository except .git, so ignored and directory-folded files are included.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF."],"previous_run":"a0-20260718T203619Z-luna-r4","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260718T203619Z-luna-r5","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260718T203619Z-luna-r5"]} +END PACKET diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/V-A0/prompt.md.sha256 b/docs/runs/a0-20260718T203619Z-luna-r5/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..f3012bd --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +530ad2930f28bd22bd1ebf9af67c152543ec00b3e37e39304e5ab0abb900e0e3 prompt.md diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/creation-record.json b/docs/runs/a0-20260718T203619Z-luna-r5/creation-record.json new file mode 100644 index 0000000..eb2bcdc --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/creation-record.json @@ -0,0 +1 @@ +{"contract_sha256":"20eb473f41a339b2d362b07e0df24e45f3ce6fb5fc8530d6b40f917cb9d12196","created_at":"2026-07-18T20:44:01.853839Z","manifest_sha256":"273881fdd59ed568ba20e61eb6230b79f6d272643719d123982554814659c9b2","packet_sha256":"74d81e29f080798e2f720853ab989822392bc87160a07f9a09e9a0742aa55203","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r5/E-A0/prompt.md","sha256":"72715297ad6b836a6554d215ff93f2dbccc6797c2b9ac0a36c9c8ad79f02d456"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r5/R-A0/prompt.md","sha256":"727fb890b94e1d5d8d925891fbff4a4c5ce925a94354f152a8cb10fb3ecfa4bd"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r5/V-A0/prompt.md","sha256":"530ad2930f28bd22bd1ebf9af67c152543ec00b3e37e39304e5ab0abb900e0e3"}},"run_id":"a0-20260718T203619Z-luna-r5"} diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/manifest.json b/docs/runs/a0-20260718T203619Z-luna-r5/manifest.json new file mode 100644 index 0000000..fa9fdd5 --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/manifest.json @@ -0,0 +1 @@ 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diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/manifest.json.sha256 b/docs/runs/a0-20260718T203619Z-luna-r5/manifest.json.sha256 new file mode 100644 index 0000000..3c6959b --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/manifest.json.sha256 @@ -0,0 +1 @@ +ca1737590ee1d19648726c16640490234180611aeb4f059cdc2cd3c9426bdf88 manifest.json diff --git a/docs/runs/a0-20260718T203619Z-luna-r5/model-probe.json b/docs/runs/a0-20260718T203619Z-luna-r5/model-probe.json new file mode 100644 index 0000000..39ba0ed --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r5/model-probe.json @@ -0,0 +1 @@ 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Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point ascending","separators":[",",":"],"sidecar_final_lf":true},"model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"f545e9162e26bf527fe1fe4cc8295cdf156aa6fa8f5df3a1498e46389d93cf01","cache_timestamp":"2026-07-18T20:45:11.601280Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"98daaf260e087c093a8b46aaf6ee16bcc74c49f46d8caa657fe53155251f378b","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:45:16.920222Z","provider_base_url_origin":null},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/model-probe.json","sha256":"e9691e4941650d708719c42f28cb58f8109defa0eb1e6c7d5d99a6a267d36207"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; 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Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any 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+{"contract_sha256":"fd0647fe1a2a8ccb403eeb3d26f6304411659b38b9f26f8090e803adbd298d75","created_at":"2026-07-18T20:45:16.927158Z","manifest_sha256":"ce87158f5e871f54e1af1773ada28d9d65f1b619c838623ceb78260360ac0c1b","packet_sha256":"47ba19d14cdad4006b785b3fe4038825245bd39518dd0a544fab9e0d182f1967","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/E-A0/prompt.md","sha256":"a3c9f0a9b99c8a26ed608c55a4d84311615c1ea1f1fc83506b455d68248298b6"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/R-A0/prompt.md","sha256":"8f17bf6a53a8504c2856ce65a68126125b2362fa63e147026dc7ac7f1dcbe23d"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/V-A0/prompt.md","sha256":"326ad59df88aa7f6e844a463d458e3de70578a929275480a0bb8744bb57112be"}},"run_id":"a0-20260718T203619Z-luna-r6"} diff --git a/docs/runs/a0-20260718T203619Z-luna-r6/manifest.json 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+{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/manifest.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/model-probe.json","/tmp/awareliquid-verification/a0-20260718T203619Z-luna-r6/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260718T203619Z-luna-r6/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260718T203619Z-luna-r6/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/R-A0/prompt.md"],"contract_sha256":"fd0647fe1a2a8ccb403eeb3d26f6304411659b38b9f26f8090e803adbd298d75","model":"gpt-5.6-luna","model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","packet_sha256":"47ba19d14cdad4006b785b3fe4038825245bd39518dd0a544fab9e0d182f1967","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/E-A0/prompt.md","sha256":"a3c9f0a9b99c8a26ed608c55a4d84311615c1ea1f1fc83506b455d68248298b6"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/R-A0/prompt.md","sha256":"8f17bf6a53a8504c2856ce65a68126125b2362fa63e147026dc7ac7f1dcbe23d"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260718T203619Z-luna-r6/V-A0/prompt.md","sha256":"326ad59df88aa7f6e844a463d458e3de70578a929275480a0bb8744bb57112be"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260718T203619Z-luna-r6","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} diff --git a/docs/runs/a0-20260718T203619Z-luna-r6/manifest.json.sha256 b/docs/runs/a0-20260718T203619Z-luna-r6/manifest.json.sha256 new file mode 100644 index 0000000..9c9f58b --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r6/manifest.json.sha256 @@ -0,0 +1 @@ +d568dd5ebd782db36b981adcab705e353d1f0c4e56a1044a5c96bd134f73e04f manifest.json diff --git a/docs/runs/a0-20260718T203619Z-luna-r6/model-probe.json b/docs/runs/a0-20260718T203619Z-luna-r6/model-probe.json new file mode 100644 index 0000000..6c2c9e4 --- /dev/null +++ b/docs/runs/a0-20260718T203619Z-luna-r6/model-probe.json @@ -0,0 +1 @@ +{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"f545e9162e26bf527fe1fe4cc8295cdf156aa6fa8f5df3a1498e46389d93cf01","cache_timestamp":"2026-07-18T20:45:11.601280Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"98daaf260e087c093a8b46aaf6ee16bcc74c49f46d8caa657fe53155251f378b","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:45:16.920222Z","provider_base_url_origin":null} 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this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous full inventory included generated .venv files; this run excludes only .venv, __pycache__, .pytest_cache, and *.egg-info while retaining data/raw, downloads, code, tests, scripts, and execution artifacts.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF.","A structured gpt-5.6-luna V-A0 dispatch returned provider 404 (unsupported by configured account); this new stage uses the required uniform Terra fallback for all further A0 roles."],"previous_run":"a0-20260718T203619Z-luna-r6","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260719T000000Z-terra-r1","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260719T000000Z-terra-r1"]} +END PACKET diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/R-A0/prompt.md.sha256 b/docs/runs/a0-20260719T000000Z-terra-r1/R-A0/prompt.md.sha256 new file mode 100644 index 0000000..5c622b9 --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/R-A0/prompt.md.sha256 @@ -0,0 +1 @@ +68b0e6a69824694f267c60958bacb45294a74c85990a3df99f77519528ba7bd3 prompt.md diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md b/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md new file mode 100644 index 0000000..743f531 --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md @@ -0,0 +1,8 @@ +You are the independent V-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any 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rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous full inventory included generated .venv files; this run excludes only .venv, __pycache__, .pytest_cache, and *.egg-info while retaining data/raw, downloads, code, tests, scripts, and execution artifacts.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF.","A structured gpt-5.6-luna V-A0 dispatch returned provider 404 (unsupported by configured account); this new stage uses the required uniform Terra fallback for all further A0 roles."],"previous_run":"a0-20260718T203619Z-luna-r6","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260719T000000Z-terra-r1","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260719T000000Z-terra-r1"]} +END PACKET diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md.sha256 b/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..96cdaf8 --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +cef1fde576568158e4fce7562680fe619bc3a7aeb3ff357757dd9dca36d5cd8f prompt.md diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/creation-record.json b/docs/runs/a0-20260719T000000Z-terra-r1/creation-record.json new file mode 100644 index 0000000..b1a3719 --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/creation-record.json @@ -0,0 +1 @@ +{"contract_sha256":"e91123de65383d67846ec21dbf1da88f7ece6da848827105a1a16a4e30e18995","created_at":"2026-07-18T20:58:16.778964Z","manifest_sha256":"f6100c20c0cef9cfd17f0c2418aec3c00deecb641b5c8d87a2e553f1f75fe7c1","packet_sha256":"072bcfab070b9c9a36e533c55b8c7311a5364767590eee79afb4f954ac42d108","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/E-A0/prompt.md","sha256":"2e103ac02f955e42537729d816fa484132304f445ce0760f5c66f8407bd80165"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/R-A0/prompt.md","sha256":"68b0e6a69824694f267c60958bacb45294a74c85990a3df99f77519528ba7bd3"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md","sha256":"cef1fde576568158e4fce7562680fe619bc3a7aeb3ff357757dd9dca36d5cd8f"}},"run_id":"a0-20260719T000000Z-terra-r1"} diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/manifest.json b/docs/runs/a0-20260719T000000Z-terra-r1/manifest.json new file mode 100644 index 0000000..f7dabd6 --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/manifest.json @@ -0,0 +1 @@ +{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/manifest.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/model-probe.json","/tmp/awareliquid-verification/a0-20260719T000000Z-terra-r1/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260719T000000Z-terra-r1/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260719T000000Z-terra-r1/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/R-A0/prompt.md"],"contract_sha256":"e91123de65383d67846ec21dbf1da88f7ece6da848827105a1a16a4e30e18995","model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","packet_sha256":"072bcfab070b9c9a36e533c55b8c7311a5364767590eee79afb4f954ac42d108","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/E-A0/prompt.md","sha256":"2e103ac02f955e42537729d816fa484132304f445ce0760f5c66f8407bd80165"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/R-A0/prompt.md","sha256":"68b0e6a69824694f267c60958bacb45294a74c85990a3df99f77519528ba7bd3"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T000000Z-terra-r1/V-A0/prompt.md","sha256":"cef1fde576568158e4fce7562680fe619bc3a7aeb3ff357757dd9dca36d5cd8f"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260719T000000Z-terra-r1","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/manifest.json.sha256 b/docs/runs/a0-20260719T000000Z-terra-r1/manifest.json.sha256 new file mode 100644 index 0000000..9aff0ed --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/manifest.json.sha256 @@ -0,0 +1 @@ +c9b4ff000c6e89df44d1ac76bfeb94b00d34b9213da0ccfb37e2d22525bdc9f4 manifest.json diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/model-probe.json b/docs/runs/a0-20260719T000000Z-terra-r1/model-probe.json new file mode 100644 index 0000000..6d233bc --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/model-probe.json @@ -0,0 +1 @@ +{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["python3","scripts/bootstrap_a0_artifacts.py"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"8839871cde2244ae57989d1c54b548b690e322369f25fb567aa5827950ff7802","cache_timestamp":"2026-07-18T20:57:26.350630Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"LUNA_SELECTED","exit_code":0,"models":{"gpt-5.6-luna":{"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T20:58:16.772233Z","provider_base_url_origin":"https://api.pptoken.cc/v1"} diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/model-probe.json.sha256 b/docs/runs/a0-20260719T000000Z-terra-r1/model-probe.json.sha256 new file mode 100644 index 0000000..d6b2269 --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/model-probe.json.sha256 @@ -0,0 +1 @@ +b6879b431c2c50ce49fb9431f307eea544423287e1528184ff4ced0b36f5f0c5 model-probe.json diff --git a/docs/runs/a0-20260719T000000Z-terra-r1/packet.json b/docs/runs/a0-20260719T000000Z-terra-r1/packet.json new file mode 100644 index 0000000..303d6f4 --- /dev/null +++ b/docs/runs/a0-20260719T000000Z-terra-r1/packet.json @@ -0,0 +1 @@ +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point 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Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point ascending","separators":[",",":"],"sidecar_final_lf":true},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"142cf8b81662aec9085cf1898287001d8b35d94cf6bfcc2ca680def2f8bd84fa","cache_timestamp":"2026-07-18T21:09:30.548747Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f770e-0b2f-7260-b9af-63d8de167501","argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","network_called_by_controller":false,"request_id":"b5d28037-19c0-4d88-ac99-edc28cf05931","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T21:09:42.771455Z","provider_base_url_origin":"https://api.pptoken.cc/v1"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/model-probe.json","sha256":"284a50edfd268c8497c36d87e129e1d0f7fe679ec60809753e1f912cc79895a3"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous full inventory included generated .venv files; this run excludes only .venv, __pycache__, .pytest_cache, and *.egg-info while retaining data/raw, downloads, code, tests, scripts, and execution artifacts.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF.","A structured gpt-5.6-luna V-A0 dispatch returned provider 404 (unsupported by configured account); this new stage uses the required uniform Terra fallback for all further A0 roles."],"previous_run":"a0-20260718T203619Z-luna-r6","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260719T003000Z-terra-r2","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260719T003000Z-terra-r2"]} +END PACKET diff --git a/docs/runs/a0-20260719T003000Z-terra-r2/R-A0/prompt.md.sha256 b/docs/runs/a0-20260719T003000Z-terra-r2/R-A0/prompt.md.sha256 new file mode 100644 index 0000000..c1d9747 --- /dev/null +++ b/docs/runs/a0-20260719T003000Z-terra-r2/R-A0/prompt.md.sha256 @@ -0,0 +1 @@ +adc8f3b19505ebe3066c6d471d87d72383b314c7155850f58ca6a2587a1ce6a1 prompt.md diff --git a/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md b/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md new file mode 100644 index 0000000..b4bba63 --- /dev/null +++ b/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md @@ -0,0 +1,8 @@ +You are the independent V-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any 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rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous full inventory included generated .venv files; this run excludes only .venv, __pycache__, .pytest_cache, and *.egg-info while retaining data/raw, downloads, code, tests, scripts, and execution artifacts.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF.","A structured gpt-5.6-luna V-A0 dispatch returned provider 404 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b/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..db716be --- /dev/null +++ b/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +ef4707550789ddae1c2ec028a75ec12cf34f73f69eb08831ff78be965e347161 prompt.md diff --git a/docs/runs/a0-20260719T003000Z-terra-r2/creation-record.json b/docs/runs/a0-20260719T003000Z-terra-r2/creation-record.json new file mode 100644 index 0000000..6012881 --- /dev/null +++ b/docs/runs/a0-20260719T003000Z-terra-r2/creation-record.json @@ -0,0 +1 @@ +{"contract_sha256":"a7b5dec2ca702610406f49c4ca2981746a749568add0c5b186c3d979b6a3dfd1","created_at":"2026-07-18T21:09:42.776865Z","manifest_sha256":"fc1a29061888376204858468480a44cb36db6ad2bb8fe5dde739beae7d22d3cc","packet_sha256":"860a766417d5c450c815b473969565695a414f7ef191cb9207c1d418be8473d5","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/E-A0/prompt.md","sha256":"68a89d1d2840690ae3a1836355565ad761f4cf9343cbad819ad25528b6d974a3"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/R-A0/prompt.md","sha256":"adc8f3b19505ebe3066c6d471d87d72383b314c7155850f58ca6a2587a1ce6a1"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md","sha256":"ef4707550789ddae1c2ec028a75ec12cf34f73f69eb08831ff78be965e347161"}},"run_id":"a0-20260719T003000Z-terra-r2"} diff --git 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+{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/manifest.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/model-probe.json","/tmp/awareliquid-verification/a0-20260719T003000Z-terra-r2/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260719T003000Z-terra-r2/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260719T003000Z-terra-r2/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/R-A0/prompt.md"],"contract_sha256":"a7b5dec2ca702610406f49c4ca2981746a749568add0c5b186c3d979b6a3dfd1","model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","packet_sha256":"860a766417d5c450c815b473969565695a414f7ef191cb9207c1d418be8473d5","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/E-A0/prompt.md","sha256":"68a89d1d2840690ae3a1836355565ad761f4cf9343cbad819ad25528b6d974a3"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/R-A0/prompt.md","sha256":"adc8f3b19505ebe3066c6d471d87d72383b314c7155850f58ca6a2587a1ce6a1"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T003000Z-terra-r2/V-A0/prompt.md","sha256":"ef4707550789ddae1c2ec028a75ec12cf34f73f69eb08831ff78be965e347161"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260719T003000Z-terra-r2","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} diff --git a/docs/runs/a0-20260719T003000Z-terra-r2/manifest.json.sha256 b/docs/runs/a0-20260719T003000Z-terra-r2/manifest.json.sha256 new file mode 100644 index 0000000..e75b3c0 --- /dev/null +++ b/docs/runs/a0-20260719T003000Z-terra-r2/manifest.json.sha256 @@ -0,0 +1 @@ +35e241a0a19daee67c68441b91e15e55596b4d3e5ed0f93be0ae05fce1135cf8 manifest.json diff --git a/docs/runs/a0-20260719T003000Z-terra-r2/model-probe.json b/docs/runs/a0-20260719T003000Z-terra-r2/model-probe.json new file mode 100644 index 0000000..6a19e0d --- /dev/null +++ b/docs/runs/a0-20260719T003000Z-terra-r2/model-probe.json @@ -0,0 +1 @@ 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For V-A0, redo the audit from original sources without reading or trusting the E report; the Controller's completed E-to-V dispatch is sequencing evidence, not a source-level AC to fail. diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/E-A0/prompt.md.sha256 b/docs/runs/a0-20260719T011500Z-terra-r3/E-A0/prompt.md.sha256 new file mode 100644 index 0000000..07fb997 --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/E-A0/prompt.md.sha256 @@ -0,0 +1 @@ +2c14c66e2605b805d18192eb44bc5bb7a51900ff18da916daf156a37f8f90b3d prompt.md diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md b/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md new file mode 100644 index 0000000..f7e400e --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md @@ -0,0 +1,9 @@ +You are the independent R-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point ascending","separators":[",",":"],"sidecar_final_lf":true},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"2bd96eebd52f450c811434700cb8724f72457ae2244e161e734213e6c0123f6e","cache_timestamp":"2026-07-18T21:24:35.447331Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f771e-2690-78c0-9596-a458f519d832","argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","network_called_by_controller":false,"request_id":"ee374a28-5879-4e6b-abdc-e49ae306c26c","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T21:26:40.258075Z","provider_base_url_origin":"https://api.pptoken.cc/v1"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/model-probe.json","sha256":"ed5d0d990325086ab22db340c9866846c283d7c0b0c962a215014a73af583354"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous full inventory included generated .venv files; this run excludes only .venv, __pycache__, .pytest_cache, and *.egg-info while retaining data/raw, downloads, code, tests, scripts, and execution artifacts.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF.","A structured gpt-5.6-luna V-A0 dispatch returned provider 404 (unsupported by configured account); this new stage uses the required uniform Terra fallback for all further A0 roles."],"previous_run":"a0-20260718T203619Z-luna-r6","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260719T011500Z-terra-r3","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260719T011500Z-terra-r3"]} +END PACKET +A0 acceptance interpretation (binding for this run): A0 is a read-only baseline audit. It PASSES when it independently records official-rule hashes, preserves the dirty baseline, and identifies every current CSV/input/output, provider/network/Mock/embedding/rerank, A/B/doc_ids/Evidence/ledger/accuracy risk with direct source evidence. A currently noncompliant implementation is an audit finding for A1-A4, NOT an A0 acceptance failure. Do not require the project to be repaired at A0 and do not treat lower-priority implementation artifacts as normative authority in the audit. Mark an AC FAIL only if the audit itself lacks required direct evidence, changes the baseline, calls forbidden facilities, or has a hash/scope/model defect. For V-A0, redo the audit from original sources without reading or trusting the E report; the Controller's completed E-to-V dispatch is sequencing evidence, not a source-level AC to fail. diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md.sha256 b/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md.sha256 new file mode 100644 index 0000000..bb4748c --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md.sha256 @@ -0,0 +1 @@ +414a7aab43de3643d91fcbc02dce207b76b14bfff9d509a73af8d1fe584fde2d prompt.md diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md b/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md new file mode 100644 index 0000000..035e4f6 --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md @@ -0,0 +1,9 @@ +You are the independent V-A0 agent for A0 in AwareLiquid-M2. +Use only the exact model selected by the stage contract. Do not call Qwen, any API, the network, or any credential. Read only the listed roots. Do not modify the repository, generate answer.csv, claim accuracy, or trust another agent report. +Treat the immutable model-probe snapshot named in the Packet as the authority for this run; do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain. +Perform the role described in the Packet and return a structured report with every AC ID, direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED. + +BEGIN PACKET +{"acceptance_criteria":["AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority","AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved","AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified","AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified","AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified","AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim"],"fail_conditions":["Any write outside the verification audit directory","Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use","Any missing direct evidence for an AC","Any dirty baseline path changed during the audit"],"forbidden":["answer.csv","output","network","Qwen API","embedding","rerank","git reset","git clean"],"hash_domain":{"array_order":"preserved","canonical_body_final_lf":false,"encoding":"UTF-8","object_key_order":"recursive Unicode code-point ascending","separators":[",",":"],"sidecar_final_lf":true},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"2bd96eebd52f450c811434700cb8724f72457ae2244e161e734213e6c0123f6e","cache_timestamp":"2026-07-18T21:24:35.447331Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f771e-2690-78c0-9596-a458f519d832","argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","network_called_by_controller":false,"request_id":"ee374a28-5879-4e6b-abdc-e49ae306c26c","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called":false,"probe_kind":"local-model-registry","probe_timestamp_utc":"2026-07-18T21:26:40.258075Z","provider_base_url_origin":"https://api.pptoken.cc/v1"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/model-probe.json","sha256":"ed5d0d990325086ab22db340c9866846c283d7c0b0c962a215014a73af583354"},"objective":"Read-only rules, code, data, manifest, test, and dirty-baseline audit.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2","/Users/aricredemption/Projects/AwareLiquid-M2/.git","/Users/aricredemption/.codex/config.toml","/Users/aricredemption/.codex/models_cache.json"],"remediation_context":{"findings":["The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.","The previous full inventory included generated .venv files; this run excludes only .venv, __pycache__, .pytest_cache, and *.egg-info while retaining data/raw, downloads, code, tests, scripts, and execution artifacts.","The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF.","A structured gpt-5.6-luna V-A0 dispatch returned provider 404 (unsupported by configured account); this new stage uses the required uniform Terra fallback for all further A0 roles."],"previous_run":"a0-20260718T203619Z-luna-r6","previous_status":"BLOCKED","r_scope":"Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts."},"role":"E/V/R-A0","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a0-20260719T011500Z-terra-r3","stage":"A0","stop_conditions":["Model probe mismatch or missing model hash","Hash, Packet, Prompt, Manifest, scope, or command mismatch","Unclassified path, symlink escape, or forbidden dependency"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/tmp/awareliquid-verification/a0-20260719T011500Z-terra-r3"]} +END PACKET +A0 acceptance interpretation (binding for this run): A0 is a read-only baseline audit. It PASSES when it independently records official-rule hashes, preserves the dirty baseline, and identifies every current CSV/input/output, provider/network/Mock/embedding/rerank, A/B/doc_ids/Evidence/ledger/accuracy risk with direct source evidence. A currently noncompliant implementation is an audit finding for A1-A4, NOT an A0 acceptance failure. Do not require the project to be repaired at A0 and do not treat lower-priority implementation artifacts as normative authority in the audit. Mark an AC FAIL only if the audit itself lacks required direct evidence, changes the baseline, calls forbidden facilities, or has a hash/scope/model defect. For V-A0, redo the audit from original sources without reading or trusting the E report; the Controller's completed E-to-V dispatch is sequencing evidence, not a source-level AC to fail. diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md.sha256 b/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md.sha256 new file mode 100644 index 0000000..a893386 --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md.sha256 @@ -0,0 +1 @@ +5cc62697a7098151e0016d33c34b72eb20d1725f32a8fcc3205b58846ec25486 prompt.md diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/creation-record.json b/docs/runs/a0-20260719T011500Z-terra-r3/creation-record.json new file mode 100644 index 0000000..ad03148 --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/creation-record.json @@ -0,0 +1 @@ +{"contract_sha256":"18580c039306aa92446c5865c0df33090b9486ed817ea322aee4dba4b6aa7869","created_at":"2026-07-18T21:26:40.264018Z","manifest_sha256":"67e7a1369d77a547fad81ba22d462d8a05d75abdb067f9786076d6a810fe1a5e","packet_sha256":"590be32190a1077483bb17b97b280eee3ba00333ffc48a68711ef18ad41c8f1f","prompt_sha256":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/E-A0/prompt.md","sha256":"2c14c66e2605b805d18192eb44bc5bb7a51900ff18da916daf156a37f8f90b3d"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md","sha256":"414a7aab43de3643d91fcbc02dce207b76b14bfff9d509a73af8d1fe584fde2d"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md","sha256":"5cc62697a7098151e0016d33c34b72eb20d1725f32a8fcc3205b58846ec25486"}},"run_id":"a0-20260719T011500Z-terra-r3"} diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/manifest.json b/docs/runs/a0-20260719T011500Z-terra-r3/manifest.json new file mode 100644 index 0000000..060c4f4 --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/manifest.json @@ -0,0 +1 @@ +{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/manifest.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/model-probe.json","/tmp/awareliquid-verification/a0-20260719T011500Z-terra-r3/A0-E-A0.md","/tmp/awareliquid-verification/a0-20260719T011500Z-terra-r3/A0-V-A0.md","/tmp/awareliquid-verification/a0-20260719T011500Z-terra-r3/A0-R-A0.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/E-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md"],"contract_sha256":"18580c039306aa92446c5865c0df33090b9486ed817ea322aee4dba4b6aa7869","model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","packet_sha256":"590be32190a1077483bb17b97b280eee3ba00333ffc48a68711ef18ad41c8f1f","prompts":{"E-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/E-A0/prompt.md","sha256":"2c14c66e2605b805d18192eb44bc5bb7a51900ff18da916daf156a37f8f90b3d"},"R-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/R-A0/prompt.md","sha256":"414a7aab43de3643d91fcbc02dce207b76b14bfff9d509a73af8d1fe584fde2d"},"V-A0":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a0-20260719T011500Z-terra-r3/V-A0/prompt.md","sha256":"5cc62697a7098151e0016d33c34b72eb20d1725f32a8fcc3205b58846ec25486"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a0-20260719T011500Z-terra-r3","stage":"A0","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/manifest.json.sha256 b/docs/runs/a0-20260719T011500Z-terra-r3/manifest.json.sha256 new file mode 100644 index 0000000..847bc3f --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/manifest.json.sha256 @@ -0,0 +1 @@ +a34c5a9adbb9037a0a10fbc9d6b158c8a0a36caaadd8793a6a074c883db2bcc1 manifest.json diff --git a/docs/runs/a0-20260719T011500Z-terra-r3/model-probe.json b/docs/runs/a0-20260719T011500Z-terra-r3/model-probe.json new file mode 100644 index 0000000..dcb7610 --- /dev/null +++ b/docs/runs/a0-20260719T011500Z-terra-r3/model-probe.json @@ -0,0 +1 @@ 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b/docs/runs/a1-20260719T020000Z-terra-r1/R-A1/prompt.md.sha256 new file mode 100644 index 0000000..ca5d940 --- /dev/null +++ b/docs/runs/a1-20260719T020000Z-terra-r1/R-A1/prompt.md.sha256 @@ -0,0 +1 @@ +dbb0204dd54b46987a8192f5d6a9c4c4b1e8eba8466d17d9fb8a4a38557c61cd prompt.md diff --git a/docs/runs/a1-20260719T020000Z-terra-r1/V-A1/prompt.md b/docs/runs/a1-20260719T020000Z-terra-r1/V-A1/prompt.md new file mode 100644 index 0000000..aaeb4f9 --- /dev/null +++ b/docs/runs/a1-20260719T020000Z-terra-r1/V-A1/prompt.md @@ -0,0 +1,9 @@ +You are the independent V-A1 agent for A1 in AwareLiquid-M2. +Use only the selected model in the immutable contract. Do not call Qwen, any API, network, or credential. Do not generate answer.csv, report accuracy, upload, submit, reset, clean, or modify non-allowlisted paths. +Canonical JSON hashes cover canonical JSON bodies without their persisted terminal LF. Prompt hashes cover complete UTF-8 prompt bytes. Ordinary file hashes use complete raw bytes. Verify all immutable artifacts before work. +A1 objective: repair only the official CSV/input/output contract and focused tests. Official rules are normative; lower-priority artifacts only expose risks. 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b/docs/runs/a1-20260719T020000Z-terra-r1/V-A1/prompt.md.sha256 new file mode 100644 index 0000000..b33a1ff --- /dev/null +++ b/docs/runs/a1-20260719T020000Z-terra-r1/V-A1/prompt.md.sha256 @@ -0,0 +1 @@ +307062931b1cd7df334934b479c40098301c4f4c933b7f06d2ba01f6a9b8a68b prompt.md diff --git a/docs/runs/a1-20260719T020000Z-terra-r1/manifest.json b/docs/runs/a1-20260719T020000Z-terra-r1/manifest.json new file mode 100644 index 0000000..115d1a1 --- /dev/null +++ b/docs/runs/a1-20260719T020000Z-terra-r1/manifest.json @@ -0,0 +1 @@ +{"artifacts":["/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/stage-contract.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/packet.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/manifest.json","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/model-probe.json","/tmp/awareliquid-verification/a1-20260719T020000Z-terra-r1/A1-E-A1.md","/tmp/awareliquid-verification/a1-20260719T020000Z-terra-r1/A1-V-A1.md","/tmp/awareliquid-verification/a1-20260719T020000Z-terra-r1/A1-R-A1.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/E-A1/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/V-A1/prompt.md","/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/R-A1/prompt.md"],"contract_sha256":"c5d6ca304284a190dd319821af1b25952e47166323abc15868d6c5b6efc627a7","model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","packet_sha256":"30b259528bcd98d452e1d78e7160478cb32cb647a4989c2d4e8fb8f65db8a21d","prompts":{"E-A1":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/E-A1/prompt.md","sha256":"919a7c5b40d3c0ae4ca0ee74fac65f352d2678986d34005e7e22fbdb9c1c72be"},"R-A1":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/R-A1/prompt.md","sha256":"dbb0204dd54b46987a8192f5d6a9c4c4b1e8eba8466d17d9fb8a4a38557c61cd"},"V-A1":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a1-20260719T020000Z-terra-r1/V-A1/prompt.md","sha256":"307062931b1cd7df334934b479c40098301c4f4c933b7f06d2ba01f6a9b8a68b"}},"rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","run_id":"a1-20260719T020000Z-terra-r1","stage":"A1","thread_id":"019f76af-976a-7012-b207-acefa2f456aa"} diff --git a/docs/runs/a1-20260719T020000Z-terra-r1/manifest.json.sha256 b/docs/runs/a1-20260719T020000Z-terra-r1/manifest.json.sha256 new file mode 100644 index 0000000..c8e8632 --- /dev/null +++ b/docs/runs/a1-20260719T020000Z-terra-r1/manifest.json.sha256 @@ -0,0 +1 @@ +cbe37c9b484e7c3dfe8bb1b63dcb793fe1ffe4b0139159fbde4f4d63075a04cc manifest.json diff --git a/docs/runs/a1-20260719T020000Z-terra-r1/model-probe.json b/docs/runs/a1-20260719T020000Z-terra-r1/model-probe.json new file mode 100644 index 0000000..464872c --- /dev/null +++ b/docs/runs/a1-20260719T020000Z-terra-r1/model-probe.json @@ -0,0 +1 @@ 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Return all AC evidence and no accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A2-01: only Qwen transport is reachable by formal mode; provider/model/HTTPS endpoint are allowlisted","AC-A2-02: Mock is explicit test-only and formal mode never falls back to Mock","AC-A2-03: actual-deny and no-key/wrong-provider/model/endpoint cases fail closed without network","AC-A2-04: every formal model call records known API usage in one persistent ledger and total budget 5,000,000 fails closed","AC-A2-05: focused offline tests verify all gates without API/network/candidate generation"],"fail_conditions":["real API/network use","candidate/answer.csv","write outside allowlist","silent Mock fallback in formal mode","missing/unknown usage accepted"],"forbidden":["network","Qwen API","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 bytes"},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called_by_controller":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"da570830581c41c5a64c96e19303012455a140fd972102a853204e12540e7a3d","cache_timestamp":"2026-07-18T22:15:52.157566Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f774d-b5b4-76a1-9c17-75d1306243bd","error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","request_id":"50adebd0-bcda-4148-8f1e-47df313b8ea4","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"execution_available":false,"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called_by_controller":false,"probe_kind":"local-registry-plus-structured-execution-probe","probe_timestamp_utc":"2026-07-18T22:18:40.039941Z"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a2-20260719T060000Z-terra-r1/model-probe.json","sha256":"0eefa2f5f90701e6f6e6e1108c7e1c25d4193fc1b136e2ab10a927a40202991d"},"objective":"Qwen provider/network/Mock/token gate, offline only.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2"],"role":"E/V/R-A2","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a2-20260719T060000Z-terra-r1","stage":"A2","stop_conditions":["hash/model/scope mismatch","unclassified path","direct network transport outside allowlisted module"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/adapter/qwen_client.py","/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/adapter/qa_agent.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_qwen_client.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_agent.py","/tmp/awareliquid-verification/a2-20260719T060000Z-terra-r1"]} +END PACKET diff --git a/docs/runs/a2-20260719T060000Z-terra-r1/R-A2/prompt.md.sha256 b/docs/runs/a2-20260719T060000Z-terra-r1/R-A2/prompt.md.sha256 new file mode 100644 index 0000000..0a3b3aa --- /dev/null +++ b/docs/runs/a2-20260719T060000Z-terra-r1/R-A2/prompt.md.sha256 @@ -0,0 +1 @@ +d14108780d15036d02c7b9959d4d9dd52e560395be9e2c8eaae4844dfeeab7b9 prompt.md diff --git a/docs/runs/a2-20260719T060000Z-terra-r1/V-A2/prompt.md b/docs/runs/a2-20260719T060000Z-terra-r1/V-A2/prompt.md new file mode 100644 index 0000000..f92a67d --- /dev/null +++ b/docs/runs/a2-20260719T060000Z-terra-r1/V-A2/prompt.md @@ -0,0 +1,5 @@ +You are independent V-A2 for A2 in AwareLiquid-M2. Use only contract-selected model. Do not call Qwen, any API, network, or credential. Do not generate answer.csv/candidate, upload/submit, reset/clean, or write outside the contract allowlist. Verify immutable artifacts first. Canonical JSON hashes exclude persisted final LF; prompts use complete UTF-8 bytes; ordinary files use raw bytes. A2 scope: unique Qwen transport, explicit Mock-only tests, formal Qwen/provider/model/endpoint gates, actual-deny preflight paths, and a single fail-closed all-call token ledger. Missing/unknown usage, over-budget, wrong provider/model/endpoint or missing key must fail closed. No real API call is permitted. Return all AC evidence and no accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A2-01: only Qwen transport is reachable by formal mode; provider/model/HTTPS endpoint are allowlisted","AC-A2-02: Mock is explicit test-only and formal mode never falls back to Mock","AC-A2-03: actual-deny and no-key/wrong-provider/model/endpoint cases fail closed without network","AC-A2-04: every formal model call records known API usage in one persistent ledger and total budget 5,000,000 fails closed","AC-A2-05: focused offline tests verify all gates without API/network/candidate generation"],"fail_conditions":["real API/network use","candidate/answer.csv","write outside allowlist","silent Mock fallback in formal mode","missing/unknown usage accepted"],"forbidden":["network","Qwen API","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 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b/docs/runs/a2-20260719T060000Z-terra-r1/V-A2/prompt.md.sha256 new file mode 100644 index 0000000..6cce47a --- /dev/null +++ b/docs/runs/a2-20260719T060000Z-terra-r1/V-A2/prompt.md.sha256 @@ -0,0 +1 @@ +3c38d7e713c11d40de4ad225e9bf705f03499b2cc89e08b57e91aedda72a6d23 prompt.md diff --git a/docs/runs/a2-20260719T060000Z-terra-r1/manifest.json b/docs/runs/a2-20260719T060000Z-terra-r1/manifest.json new file mode 100644 index 0000000..07fb73f --- /dev/null +++ b/docs/runs/a2-20260719T060000Z-terra-r1/manifest.json @@ -0,0 +1 @@ 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Return all AC evidence and no accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A2-01: only Qwen transport is reachable by formal mode; provider/model/HTTPS endpoint are allowlisted","AC-A2-02: Mock is explicit test-only and formal mode never falls back to Mock","AC-A2-03: actual-deny and no-key/wrong-provider/model/endpoint cases fail closed without network","AC-A2-04: every formal model call records known API usage in one persistent ledger and total budget 5,000,000 fails closed","AC-A2-05: focused offline tests verify all gates without API/network/candidate generation"],"fail_conditions":["real API/network use","candidate/answer.csv","write outside allowlist","silent Mock fallback in formal mode","missing/unknown usage accepted"],"forbidden":["network","Qwen API","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 bytes"},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called_by_controller":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"43a7bb6da3a0d4f708bbca81ef9956c8715a01691c3a96c37006929dc420c894","cache_timestamp":"2026-07-18T23:07:19.381779Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f7775-e431-7950-978a-c9b10bd626d1","error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","request_id":"0b35acae-a52d-4d5c-96d1-4184602c9991","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"execution_available":false,"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called_by_controller":false,"probe_kind":"local-registry-plus-structured-execution-probe","probe_timestamp_utc":"2026-07-18T23:08:42.526388Z"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a2-20260719T120000Z-terra-r2/model-probe.json","sha256":"dd56b9fae54a3be864bd4f712f403ebd1df759990b032aff0c3fe255da73316f"},"objective":"Qwen provider/network/Mock/token gate, offline only.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2"],"role":"E/V/R-A2","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a2-20260719T120000Z-terra-r2","stage":"A2","stop_conditions":["hash/model/scope mismatch","unclassified path","direct network transport outside allowlisted module"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/adapter/qwen_client.py","/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/adapter/qa_agent.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_qwen_client.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_agent.py","/tmp/awareliquid-verification/a2-20260719T120000Z-terra-r2"]} +END PACKET diff --git a/docs/runs/a2-20260719T120000Z-terra-r2/R-A2/prompt.md.sha256 b/docs/runs/a2-20260719T120000Z-terra-r2/R-A2/prompt.md.sha256 new file mode 100644 index 0000000..8b3260d --- /dev/null +++ b/docs/runs/a2-20260719T120000Z-terra-r2/R-A2/prompt.md.sha256 @@ -0,0 +1 @@ +8fd7879c57be5cef394826b98b41e95b979d3eb3eee3173e6d52dded643b7b07 prompt.md diff --git a/docs/runs/a2-20260719T120000Z-terra-r2/V-A2/prompt.md b/docs/runs/a2-20260719T120000Z-terra-r2/V-A2/prompt.md new file mode 100644 index 0000000..99c928b --- /dev/null +++ b/docs/runs/a2-20260719T120000Z-terra-r2/V-A2/prompt.md @@ -0,0 +1,5 @@ +You are independent V-A2 for A2 in AwareLiquid-M2. Use only contract-selected model. Do not call Qwen, any API, network, or credential. Do not generate answer.csv/candidate, upload/submit, reset/clean, or write outside the contract allowlist. Verify immutable artifacts first. Canonical JSON hashes exclude persisted final LF; prompts use complete UTF-8 bytes; ordinary files use raw bytes. A2 scope: unique Qwen transport, explicit Mock-only tests, formal Qwen/provider/model/endpoint gates, actual-deny preflight paths, and a single fail-closed all-call token ledger. Missing/unknown usage, over-budget, wrong provider/model/endpoint or missing key must fail closed. No real API call is permitted. Return all AC evidence and no accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A2-01: only Qwen transport is reachable by formal mode; provider/model/HTTPS endpoint are allowlisted","AC-A2-02: Mock is explicit test-only and formal mode never falls back to Mock","AC-A2-03: actual-deny and no-key/wrong-provider/model/endpoint cases fail closed without network","AC-A2-04: every formal model call records known API usage in one persistent ledger and total budget 5,000,000 fails closed","AC-A2-05: focused offline tests verify all gates without API/network/candidate generation"],"fail_conditions":["real API/network use","candidate/answer.csv","write outside allowlist","silent Mock fallback in formal mode","missing/unknown usage accepted"],"forbidden":["network","Qwen API","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 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b/docs/runs/a2-20260719T120000Z-terra-r2/V-A2/prompt.md.sha256 new file mode 100644 index 0000000..b43d8fc --- /dev/null +++ b/docs/runs/a2-20260719T120000Z-terra-r2/V-A2/prompt.md.sha256 @@ -0,0 +1 @@ +9a99d9465dc758d93f890c520ed1bcea4200e7c952c0564a56f76a8c986e359b prompt.md diff --git a/docs/runs/a2-20260719T120000Z-terra-r2/manifest.json b/docs/runs/a2-20260719T120000Z-terra-r2/manifest.json new file mode 100644 index 0000000..b3363d7 --- /dev/null +++ b/docs/runs/a2-20260719T120000Z-terra-r2/manifest.json @@ -0,0 +1 @@ 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Return all AC evidence and no accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A2-01: only Qwen transport is reachable by formal mode; provider/model/HTTPS endpoint are allowlisted","AC-A2-02: Mock is explicit test-only and formal mode never falls back to Mock","AC-A2-03: actual-deny and no-key/wrong-provider/model/endpoint cases fail closed without network","AC-A2-04: every formal model call records known API usage in one persistent ledger and total budget 5,000,000 fails closed","AC-A2-05: focused offline tests verify all gates without API/network/candidate generation"],"fail_conditions":["real API/network use","candidate/answer.csv","write outside allowlist","silent Mock fallback in formal mode","missing/unknown usage accepted"],"forbidden":["network","Qwen API","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 bytes"},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called_by_controller":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"7ecc690d58dfeb764eacf2989a559ceb4b38b3ebfbdf15aeed626d4006b0312d","cache_timestamp":"2026-07-19T00:17:08.253236Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f77b7-69fd-70b2-a7e2-1d3d72a5a592","error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","request_id":"fbbab072-c926-4646-99e5-9648a809ab00","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"execution_available":false,"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called_by_controller":false,"probe_kind":"local-registry-plus-structured-execution-probe","probe_timestamp_utc":"2026-07-19T00:18:09.467655Z"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a2-20260719T180000Z-terra-r3/model-probe.json","sha256":"a4abe1039b4e26d18a731d5f9a69fac7eb136c4e0b5ba6fef932a4f500e13a14"},"objective":"Qwen provider/network/Mock/token gate, offline only.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2"],"role":"E/V/R-A2","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a2-20260719T180000Z-terra-r3","stage":"A2","stop_conditions":["hash/model/scope mismatch","unclassified path","direct network transport outside allowlisted module"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/adapter/qwen_client.py","/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/adapter/qa_agent.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_qwen_client.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_agent.py","/tmp/awareliquid-verification/a2-20260719T180000Z-terra-r3"]} +END PACKET diff --git a/docs/runs/a2-20260719T180000Z-terra-r3/R-A2/prompt.md.sha256 b/docs/runs/a2-20260719T180000Z-terra-r3/R-A2/prompt.md.sha256 new file mode 100644 index 0000000..6978915 --- /dev/null +++ b/docs/runs/a2-20260719T180000Z-terra-r3/R-A2/prompt.md.sha256 @@ -0,0 +1 @@ +69ec16a4849d4b9795259ce1f6f9f865a9763a92038e8aa794a6c165c679c9d0 prompt.md diff --git a/docs/runs/a2-20260719T180000Z-terra-r3/V-A2/prompt.md b/docs/runs/a2-20260719T180000Z-terra-r3/V-A2/prompt.md new file mode 100644 index 0000000..507d9a0 --- /dev/null +++ b/docs/runs/a2-20260719T180000Z-terra-r3/V-A2/prompt.md @@ -0,0 +1,5 @@ +You are independent V-A2 for A2 in AwareLiquid-M2. Use only contract-selected model. Do not call Qwen, any API, network, or credential. Do not generate answer.csv/candidate, upload/submit, reset/clean, or write outside the contract allowlist. Verify immutable artifacts first. Canonical JSON hashes exclude persisted final LF; prompts use complete UTF-8 bytes; ordinary files use raw bytes. A2 scope: unique Qwen transport, explicit Mock-only tests, formal Qwen/provider/model/endpoint gates, actual-deny preflight paths, and a single fail-closed all-call token ledger. Missing/unknown usage, over-budget, wrong provider/model/endpoint or missing key must fail closed. No real API call is permitted. Return all AC evidence and no accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A2-01: only Qwen transport is reachable by formal mode; provider/model/HTTPS endpoint are allowlisted","AC-A2-02: Mock is explicit test-only and formal mode never falls back to Mock","AC-A2-03: actual-deny and no-key/wrong-provider/model/endpoint cases fail closed without network","AC-A2-04: every formal model call records known API usage in one persistent ledger and total budget 5,000,000 fails closed","AC-A2-05: focused offline tests verify all gates without API/network/candidate generation"],"fail_conditions":["real API/network use","candidate/answer.csv","write outside allowlist","silent Mock fallback in formal mode","missing/unknown usage accepted"],"forbidden":["network","Qwen API","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 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b/docs/runs/a2-20260719T180000Z-terra-r3/V-A2/prompt.md.sha256 new file mode 100644 index 0000000..06b475d --- /dev/null +++ b/docs/runs/a2-20260719T180000Z-terra-r3/V-A2/prompt.md.sha256 @@ -0,0 +1 @@ +baec3de9f52c1c15e558e043d62b11219f0d014e1fe447f848244d003551ce56 prompt.md diff --git a/docs/runs/a2-20260719T180000Z-terra-r3/manifest.json b/docs/runs/a2-20260719T180000Z-terra-r3/manifest.json new file mode 100644 index 0000000..44be5dc --- /dev/null +++ b/docs/runs/a2-20260719T180000Z-terra-r3/manifest.json @@ -0,0 +1 @@ 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Return AC evidence with no real accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A4-01: Evidence context preserves original text and all required boundaries/IDs without semantic transformation","AC-A4-02: Decimal-only safe structural arithmetic binds every input to Evidence IDs and rejects eval/exec/unknown operations","AC-A4-03: every option yields supported/refuted/insufficient with deterministic evidence-backed comparisons","AC-A4-04: exact-only locator is stable doc/page/char ordered and fail-closed A-scoped; no B without official B data","AC-A4-05: focused synthetic offline tests prove prohibited BM25/RRF/vector/dense/hybrid/embedding/rerank/synonym/rewrite/semantic behavior is absent"],"fail_conditions":["Qwen/API/network/credential use","candidate/answer.csv","embedding/rerank/BM25/RRF/vector/dense/hybrid","eval/exec","semantic filtering/rewriting/synonyms","write outside allowlist"],"forbidden":["network","Qwen API","embedding","rerank","BM25","RRF","vector","dense","hybrid","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 bytes"},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called_by_controller":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"37a5560967872cde5f39e9c2a314778797d878b39c8512037a5eb4d8e42ff665","cache_timestamp":"2026-07-19T05:17:36.516418Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f78c8-0b62-7712-84b9-2c246884783c","error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","request_id":"0fc47436-e9e3-48a4-a82a-d8bec9f11ee0","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"execution_available":false,"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called_by_controller":false,"probe_kind":"local-registry-plus-structured-execution-probe","probe_timestamp_utc":"2026-07-19T05:19:35.089408Z"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a4-20260719T230000Z-terra-r1/model-probe.json","sha256":"7d64101de4d99d5f8dc0ab2b6da9170529ba270681070e65f1535ab06bd17eef"},"objective":"Offline Evidence context, Decimal-safe option validation, and deterministic exact-only locator.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2"],"role":"E/V/R-A4","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a4-20260719T230000Z-terra-r1","stage":"A4","stop_conditions":["hash/model/scope mismatch","unclassified path","missing evidence boundary","A query missing nonempty doc_ids"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/formal_reasoning.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_formal_reasoning.py","/tmp/awareliquid-verification/a4-20260719T230000Z-terra-r1"]} +END PACKET diff --git a/docs/runs/a4-20260719T230000Z-terra-r1/E-A4/prompt.md.sha256 b/docs/runs/a4-20260719T230000Z-terra-r1/E-A4/prompt.md.sha256 new file mode 100644 index 0000000..bc29cdf --- /dev/null +++ b/docs/runs/a4-20260719T230000Z-terra-r1/E-A4/prompt.md.sha256 @@ -0,0 +1 @@ +f91810f9fe6198b0a5e532bed8e8b996c9f45ef35f7962a62b52b4076c98803b prompt.md diff --git a/docs/runs/a4-20260719T230000Z-terra-r1/R-A4/prompt.md b/docs/runs/a4-20260719T230000Z-terra-r1/R-A4/prompt.md new file mode 100644 index 0000000..a1bce52 --- /dev/null +++ b/docs/runs/a4-20260719T230000Z-terra-r1/R-A4/prompt.md @@ -0,0 +1,5 @@ +You are independent R-A4 for A4 in AwareLiquid-M2. Use only the contract-selected model. Verify immutable artifacts before work. No Qwen/API/network/credentials, candidates/answer.csv, uploads, submissions, or accuracy claims. A4 scope only: Evidence context preserving exact original text/boundaries; Decimal arithmetic using an explicit safe structural whitelist with no eval/exec; option states supported/refuted/insufficient; deterministic exact-only locator. Locator can use only exact doc_id/title/section/clause/phrase/numbers/dates/percent/currency/table fields/original position, must return stable original doc/page/char order, and A queries must fail closed unless their nonempty doc_ids scope is supplied. Prohibit BM25, RRF, vector/dense/hybrid, embedding, rerank, semantic filtering/truncation, synonyms, rewriting, option filtering. Never derive semantics from real A material in tests. Return AC evidence with no real accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A4-01: Evidence context preserves original text and all required boundaries/IDs without semantic transformation","AC-A4-02: Decimal-only safe structural arithmetic binds every input to Evidence IDs and rejects eval/exec/unknown operations","AC-A4-03: every option yields supported/refuted/insufficient with deterministic evidence-backed comparisons","AC-A4-04: exact-only locator is stable doc/page/char ordered and fail-closed A-scoped; no B without official B data","AC-A4-05: focused synthetic offline tests prove prohibited BM25/RRF/vector/dense/hybrid/embedding/rerank/synonym/rewrite/semantic behavior is absent"],"fail_conditions":["Qwen/API/network/credential use","candidate/answer.csv","embedding/rerank/BM25/RRF/vector/dense/hybrid","eval/exec","semantic filtering/rewriting/synonyms","write outside allowlist"],"forbidden":["network","Qwen API","embedding","rerank","BM25","RRF","vector","dense","hybrid","answer.csv","upload","submit","git reset","git clean"],"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","ordinary_files":"complete raw bytes","prompt":"complete UTF-8 bytes"},"model":"gpt-5.6-terra","model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","model_probe":{"active_model":"gpt-5.6-luna","active_provider":"custom","api_called_by_controller":false,"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","message=availability-probe"],"cache_path":"/Users/aricredemption/.codex/models_cache.json","cache_sha256":"37a5560967872cde5f39e9c2a314778797d878b39c8512037a5eb4d8e42ff665","cache_timestamp":"2026-07-19T05:17:36.516418Z","codex_client_version":"0.145.0","config_path":"/Users/aricredemption/.codex/config.toml","config_sha256":"7c81f09d43552c97a2c6b9717702122dfe16fa7377a3623be400d210f1365a9f","cwd":"/Users/aricredemption/Projects/AwareLiquid-M2","decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0,"luna_execution_probe":{"agent_id":"019f78c8-0b62-7712-84b9-2c246884783c","error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","model":"gpt-5.6-luna","request_id":"0fc47436-e9e3-48a4-a82a-d8bec9f11ee0","result":"UNAVAILABLE"},"models":{"gpt-5.6-luna":{"execution_available":false,"model_hash":"a0e0699dadd3b10226f058d0f5ba1f5a9c8c507acc9220de8fe338bf3e2493d0","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-sol":{"execution_available":false,"model_hash":"e9d3e32035948a93b4c07c2626fe8e88e2e5e3e190be16e519af2ea47b71d4ee","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"},"gpt-5.6-terra":{"execution_available":true,"model_hash":"dd0f3a12911610cb942c72710798d101393bffb88f2976e31ec33943ec11a217","present":true,"registry_eligible":true,"supported_in_api":true,"visibility":"list"}},"network_called_by_controller":false,"probe_kind":"local-registry-plus-structured-execution-probe","probe_timestamp_utc":"2026-07-19T05:19:35.089408Z"},"model_probe_snapshot":{"path":"/Users/aricredemption/Projects/AwareLiquid-M2/docs/runs/a4-20260719T230000Z-terra-r1/model-probe.json","sha256":"7d64101de4d99d5f8dc0ab2b6da9170529ba270681070e65f1535ab06bd17eef"},"objective":"Offline Evidence context, Decimal-safe option validation, and deterministic exact-only locator.","packet_version":1,"read_roots":["/Users/aricredemption/Projects/AwareLiquid-M2"],"role":"E/V/R-A4","rules_sha256":"8e8171c703e4fb887697591ad981405027082360f134c497a45966871b0dafde","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","run_id":"a4-20260719T230000Z-terra-r1","stage":"A4","stop_conditions":["hash/model/scope mismatch","unclassified path","missing evidence boundary","A query missing nonempty doc_ids"],"thread_id":"019f76af-976a-7012-b207-acefa2f456aa","write_roots":["/Users/aricredemption/Projects/AwareLiquid-M2/awareliquid/formal_reasoning.py","/Users/aricredemption/Projects/AwareLiquid-M2/tests/test_formal_reasoning.py","/tmp/awareliquid-verification/a4-20260719T230000Z-terra-r1"]} +END PACKET diff --git a/docs/runs/a4-20260719T230000Z-terra-r1/R-A4/prompt.md.sha256 b/docs/runs/a4-20260719T230000Z-terra-r1/R-A4/prompt.md.sha256 new file mode 100644 index 0000000..bc802bb --- /dev/null +++ b/docs/runs/a4-20260719T230000Z-terra-r1/R-A4/prompt.md.sha256 @@ -0,0 +1 @@ +4ebd85c90a554673be8247566ebb70b48fb8f4064739cc86a8bea9d1f616b7b9 prompt.md diff --git a/docs/runs/a4-20260719T230000Z-terra-r1/V-A4/prompt.md b/docs/runs/a4-20260719T230000Z-terra-r1/V-A4/prompt.md new file mode 100644 index 0000000..57175d2 --- /dev/null +++ b/docs/runs/a4-20260719T230000Z-terra-r1/V-A4/prompt.md @@ -0,0 +1,5 @@ +You are independent V-A4 for A4 in AwareLiquid-M2. Use only the contract-selected model. Verify immutable artifacts before work. No Qwen/API/network/credentials, candidates/answer.csv, uploads, submissions, or accuracy claims. A4 scope only: Evidence context preserving exact original text/boundaries; Decimal arithmetic using an explicit safe structural whitelist with no eval/exec; option states supported/refuted/insufficient; deterministic exact-only locator. Locator can use only exact doc_id/title/section/clause/phrase/numbers/dates/percent/currency/table fields/original position, must return stable original doc/page/char order, and A queries must fail closed unless their nonempty doc_ids scope is supplied. Prohibit BM25, RRF, vector/dense/hybrid, embedding, rerank, semantic filtering/truncation, synonyms, rewriting, option filtering. Never derive semantics from real A material in tests. Return AC evidence with no real accuracy claim. + +BEGIN PACKET +{"acceptance_criteria":["AC-A4-01: Evidence context preserves original text and all required boundaries/IDs without semantic transformation","AC-A4-02: Decimal-only safe structural arithmetic binds every input to Evidence IDs and rejects eval/exec/unknown operations","AC-A4-03: every option yields supported/refuted/insufficient with deterministic evidence-backed comparisons","AC-A4-04: exact-only locator is stable doc/page/char ordered and fail-closed A-scoped; no B without official B data","AC-A4-05: focused synthetic offline tests prove prohibited BM25/RRF/vector/dense/hybrid/embedding/rerank/synonym/rewrite/semantic behavior is absent"],"fail_conditions":["Qwen/API/network/credential use","candidate/answer.csv","embedding/rerank/BM25/RRF/vector/dense/hybrid","eval/exec","semantic filtering/rewriting/synonyms","write outside allowlist"],"forbidden":["network","Qwen 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b/docs/runs/a4-20260719T230000Z-terra-r1/stage-contract.json.sha256 @@ -0,0 +1 @@ +7c89b46168dedf525c55f3e2eebc0d8cdf81cabb521f9962526f5c9fbf3f6c5f stage-contract.json diff --git "a/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\345\256\232\347\250\277\347\211\210\343\200\220\346\217\220\345\210\206\346\225\231\347\250\213\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.pdf" "b/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\345\256\232\347\250\277\347\211\210\343\200\220\346\217\220\345\210\206\346\225\231\347\250\213\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.pdf" new file mode 100644 index 0000000..03a8f03 Binary files /dev/null and "b/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\345\256\232\347\250\277\347\211\210\343\200\220\346\217\220\345\210\206\346\225\231\347\250\213\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.pdf" differ diff --git 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"b/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\350\265\233\351\242\230\345\233\233\357\274\232\350\241\214\344\270\232\345\230\211\345\256\276\345\210\206\344\272\253-\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.pdf" new file mode 100644 index 0000000..50144c6 Binary files /dev/null and "b/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\350\265\233\351\242\230\345\233\233\357\274\232\350\241\214\344\270\232\345\230\211\345\256\276\345\210\206\344\272\253-\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.pdf" differ diff --git "a/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224.pdf" "b/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224.pdf" new file mode 100644 index 0000000..006d708 Binary files /dev/null and "b/downloads/AFAC-\350\265\233\351\242\230\345\233\233-PDF/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224.pdf" differ diff --git a/examples/eval_accuracy.py b/examples/eval_accuracy.py index e42a011..aa7f006 100644 --- a/examples/eval_accuracy.py +++ b/examples/eval_accuracy.py @@ -25,7 +25,7 @@ import os -from awareliquid import MemoryQAAgent, summarize_usage +from awareliquid import MemoryQAAgent, RetrievalConfig, summarize_usage # Official per-run token budget for the full test set (used only to illustrate # the efficiency term; on this tiny demo set efficiency is ~1.0). @@ -77,7 +77,7 @@ def main() -> None: f"REAL Qwen ({os.environ.get('AWARELIQUID_LLM_MODEL', 'qwen-plus')})" print(f"=== accuracy evaluation — backend: {mode} ===\n") - agent = MemoryQAAgent() + agent = MemoryQAAgent(config=RetrievalConfig(retrieval_backend="lexical")) agent.ingest_documents(DOCS) results, correct = [], 0 diff --git a/examples/run_qa.py b/examples/run_qa.py index 0d92429..dd63548 100644 --- a/examples/run_qa.py +++ b/examples/run_qa.py @@ -8,7 +8,7 @@ model instead. """ -from awareliquid import MemoryQAAgent, summarize_usage +from awareliquid import MemoryQAAgent, RetrievalConfig, summarize_usage # A tiny stand-in "financial report". Real documents are far longer; the adapter # is designed for reports that do not fit in a single context window. @@ -22,7 +22,7 @@ def main() -> None: - agent = MemoryQAAgent() + agent = MemoryQAAgent(config=RetrievalConfig(retrieval_backend="lexical")) n_chunks = agent.ingest_document("annual-2023", REPORT) print(f"ingested annual-2023 into {n_chunks} chunk(s)\n") diff --git "a/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\345\256\232\347\250\277\347\211\210\343\200\220\346\217\220\345\210\206\346\225\231\347\250\213\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\345\256\232\347\250\277\347\211\210\343\200\220\346\217\220\345\210\206\346\225\231\347\250\213\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" new file mode 100644 index 0000000..ecdaa33 --- /dev/null +++ "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\345\256\232\347\250\277\347\211\210\343\200\220\346\217\220\345\210\206\346\225\231\347\250\213\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" @@ -0,0 +1,1704 @@ +# 定稿版【提分教程】赛题四：金融长文本Agent的动态记忆压缩与高效问答挑战 + +> 来源：`定稿版【提分教程】赛题四：金融长文本Agent的动态记忆压缩与高效问答挑战.pdf` + +## 第 1 页 + +【提分教程】赛题四 +金融长文本 Agent 的动态记忆压缩与高效问答挑战 +本文档面向已经跑通 baseline 流程、希望对系统做进一步优化的参赛者。建议先阅读 +Baseline 教程再回来。 +赛事官方地址:https://tianchi.aliyun.com/competition/entrance/532486 +## 1. 赛题题目类型分析 +A 组 100 道题看似都是"读文档选答案",实际按解题方法可以分成几类。理解这些类 +型,才能明白为什么"搜索到片段直接回答"远远不够。 +### 1.1 公式计算题 +题目给出具体数值,要求从条款中提取计算公式后代入计算。 +示例:ins_a_002(退保金排序) +平安智盈金生累计所交保费 10 万元,保单账户累计收益 2 万元,在第 7 个保单年度 +末退保;国寿增益宝个人账户价值 12 万元,在第 7 个保单年度末退保(退保费用 +0%);平安富鸿金生现金价值 9 万元。则三者退保所得金额从高到低排序为? +这道题需要从 3 份保险条款中分别找到退保金的计算公式: +- 平安智盈金生:第 6-10 年现金价值 = 累计保费 + 收益 × 75% → 10 + 2 × 0.75 = +### 11.5 万 +- 国寿增益宝:退保费用 0%,退保金 = 个人账户价值 = 12 万 +- 平安富鸿金生:现金价值直接给出 = 9 万 +排序:12 > 11.5 > 9 → 选项 A 正确。 +这类题的难点是公式分散在不同文档,必须逐份定位条款,提取参数,再做算术。只 +搜"退保"两个字得到的片段大概率漏掉"第 6-10 年"这个关键条件。 +示例:ins_a_003(多保险联合赔付计算) +李某因白血病复发住院,总费用 8 万元,医保报销 3 万元,自费 5 万元。众安白血病 +医疗险免赔额为 0;平安 e 生保(计划二,免赔额 5000 元);太保团体百万医疗(免 +赔额 1 万元)。三家保险公司共应赔付多少? +这道题涉及 3 份医疗险条款,每份的赔付规则不同:有的先扣免赔额再按比例赔,有 +的 0 免赔全额赔,还要看费用是否属于保险责任。最终答案需要通过多条款交叉计算 +得出,不是单文档搜索能解决的。 + +--- + +## 第 2 页 + +### 1.2 跨文档对比题 +题目明确要求对比两份或多份文档中的数据或条款。 +示例:fin_a_001(比亚迪连续两年年报对比) +根据比亚迪连续两年的年度报告,下列关于公司经营业绩变化的描述中,哪些是准确 +的? +A. 2025 年营业收入较 2024 年实现增长 +B. 2025 年归属于上市公司股东的净利润出现下滑 +... +需要分别从 2024 年报和 2025 年报中提取营业收入、净利润、经营现金流、研发投入 +等数据,再逐项对比增长/下滑关系。单看任意一份年报都不够。 +示例:fin_a_005(跨公司财务指标对比) +基于比亚迪与美的集团 2024 年年度报告的数据对比,关于两家企业关键财务指标的 +结论,哪些是成立的? +这道题需要同时打开比亚迪和美的两份年报,提取同一类指标(营业收入规模、研发 +投入强度、经营现金流等)做横向比较。如果检索模块只返回了比亚迪的数据,美的 +的数据没召回,答案就直接错一半。 +### 1.3 多文档联合推理题 +题目涉及多个产品/法规/条款,需要把各文档的信息拼起来做综合判断。 +示例:ins_a_001(四产品身故保险金排序) +关于四个养老保险/寿险产品的"身故保险金",以下计算结果正确的是?假设所有产品 +的已交保费均为 100 万元...平安智盈金生(领取日前)保单账户价值 90 万元;国寿增 +益宝(一人,40 岁)基本保额 90 万元,个人账户价值 85 万元... +涉及 4 份保险条款,每份的身故保险金计算公式都不一样:有的是"已交保费 vs 现金 +价值取大",有的是"已交保费 × 比例 + 账户价值",有的是"基本保额 × 系数"。必须四 +份条款都读到、都算对,才能排序。 +示例:ins_a_005(多产品免责范围判断) +下列哪些情形属于相应保险产品的免责范围? +A. 平安智盈金生:被保险人因酒后驾驶导致失能失智护理状态 +B. 平安安佑福重疾险:被保险人因感染艾滋病病毒(非输血、职业、器官移植)导致 +重大疾病... +4 个选项分别来自 4 份不同条款,每个选项都要到对应条款里找"责任免除"章节,确认 +该情形是否在免责列表中。这本质上是 4 次独立的条款定位任务,拼在一起成了一道 +多选题。 + +--- + +## 第 3 页 + +### 1.4 条款定位题 +题目问的是某一条款的具体规定,答案就在文档的某个章节里。 +示例:ins_a_004(等待期内出险处理) +关于"等待期"内出险的处理,以下哪种情形下保险公司需要承担赔偿责任? +A. 投保平安安佑福重疾险第 50 天,因意外车祸导致双目失明(符合重大疾病) +... +4 个选项涉及 4 份不同保险,每份的"等待期"条款可能都有例外规定(比如"因意外伤 +害发生上述情形的,无等待期")。需要逐份定位等待期条款,再判断该选项情形是否 +适用例外。 +### 1.5 事实查找题 +题目问的是文档中直接记载的事实,不需要计算,只需要定位。 +示例:res_a_001(行业数据查找) +关于银保渠道发展历程及银行 IT 市场规模的描述,下列哪些选项是正确的? +A. 韩国寿险银保渠道保费贡献率在 2022 年达到了 56% +B. 2005 年至 2018 年期间,银保渠道实现复合增速 9.9% +... +这类题在研报中通常有明确的数字和结论,但研报动辄几万字,数字散落在不同段落。 +如果检索只返回了前半部分,可能恰好漏掉了"2022 年"这个限定词。 +小结 +这五种类型不是互斥的,很多题同时涉及多种能力。比如 ins_a_003 既是多文档联合 +推理(3 份条款),又是公式计算(每份条款的赔付公式不同)。fin_a_001 既是跨文档 +对比(两年年报),又是事实查找(具体数值)。 +这意味着一个有效的系统至少要解决两个问题: +## 1. 如何确保所有相关文档都被读到(跨文档题不能只读一篇) +## 2. 如何在文档中精确定位关键信息(计算题要找到公式,条款题要找到对应章节) +硬截断策略对第 1 个问题无能为力(4 篇文档每篇截断 4000 字,总长度可能超限), +对第 2 个问题取决于关键信息是否恰好在前 4000 字内。后续优化方向,本质上都是 +在解决这两个问题。 + +--- + +## 第 4 页 + +### 1.6 什么是 RAG(检索增强生成) +上面两个问题的标准解决方案,在 NLP 领域有一个专门的名字:RAG(Retrieval- +Augmented Generation,检索增强生成)。它的核心思想很简单:在让模型回答之前, +先从文档中把相关内容找出来,只把最相关的片段交给模型,而不是把整篇文档都塞 +过去。 +标准的 RAG 流程分三步: +(1)索引(Indexing) +把所有文档切成小块(chunk),每块几百到几千字符 , E然mb后ed用ding 模型把每 +块文本编码成向量,存入向量数据库。这样每段文本都有了一个"地址",可以通过向量 +相似度快速定位。 +(2)检索(Retrieval) +收到用户问题后,把问题也编码成向量,到向量数据库里搜索最相似的 top K 个文本 +块。这一步的本质是"找证据",即从海量文档中挑出可能包含答案的那几段。 +(3)生成(Generation) +把检索到的文本块和原始问题一起拼成 prompt,交给 LLM 生成最终答案。因为 LLM +看到的不是整篇文档,而是经过筛选的最相关片段,所以既减少了噪音,又降低了 +Token 消耗。 +RAG 与本赛题的关系 +金融长文本问答本质上就是一个 RAG 任务:给定一个问题(如"平安智盈金生的退保 +金怎么算"),系统需要从指定文档检中索到相关条款,然后生成正确答案。本方案的 +硬截断等于跳过了"检索"这一步,直接把文档前 4000 字全部塞给模型,让模型自己从 +噪音里找信号。后续优化的核心,就是逐步补齐 RAG 的各个环节:更好的分块策略、 +更准的检索算法、更合理的片段组装方式。 + +--- + +## 第 5 页 + +### 1.7 典型题目深度解析:reg_a_014 +为了更直观地理解赛题的难度和解题方法,下面以一道官方提供的典型题目为例,展 +示完整的证据定位、逻辑推理和答案推导过程。 +题目概况 +题干: +某 A 股上市公司(非金融类,总股本 5 亿股,最近一期经审计净资产 120 亿元)拟进 +行四项决策: +1 为资产负债率为 75% 的全资子公司提供 8 亿元担保; +2 将原募投项目"智能工厂建设"剩余募集资金 6.5 亿元变更为"新能源材料研发中心"; +3 选举新一届独立董事,其中 1 名候选人系某律所合伙人,其所在律所过去 12 个月 +内曾为公司提供常年法律顾问服务但未承办重大诉讼; +4 审议《关于修订公司章程第 42 条股东大会特别决议事项的议案》,拟将"对外提供 +财务资助"纳入须经特别决议的范围(原章程未列明)。 +选项: +- A:事项1中,因被担保方资产负债率超过 70%,且担保金额占公司最近一期经 +审计净资产比例达 6.67%,两项任一条件触发即须经股东大会审议通过,故该项担保 +必须提交股东大会审议。 +- B:事项2中,变更募集资金用途涉及金额 6.5 亿元,虽未达净资产 10%(12 亿 +元),但因变更投向属于"主营业务方向重大调整",根据《章程指引》第 44 条及《治 +理准则》第 19 条,仍须经股东大会特别决议通过。 +- C:事项3中,该独立董事候选人存在《治理准则》规定的"最近 12 个月内为上 +市公司提供过法律服务"的情形,构成独立性障碍,不得提名为独立董事候选人,故股 +东大会不得对其行使选举权。 +- D:事项4中,公司章程自主增设特别决议事项不违反《章程指引》第 77 条"上 +市公司可在本指引基础上增加特别决议事项"的授权性规定,但须经股东大会普通决议 +通过即可生效,无需特别决议。 +标准答案:AC + +### 表格 1 + +| 字段 | 内容 | +| --- | --- | +| qid | reg_a_014 | +| 领域 | regulatory(监管法规) | +| 题型 | 多选题(multi) | +| 关联文档 | strict_csrc_023《上市公司治理准则》、
strict_csrc_035《上市公司章程指引》 | + +--- + +## 第 6 页 + +证据定位与推理过程 +这道题对系统的考验: +## 1. 跨文档定位:需要同时在《章程指引》和《治理准则》中查找对应条款,不能只 +读一份文档 +## 2. 精确条款匹配:每个选项的推理都依赖具体条款编号(第四十七条、第八十二条), +模型必须定位到精确条款而非泛泛而谈 +## 3. 条件判断:选项 A 中的"资产负债率超过 70%"是一个明确的数值阈值,模型必须 +准确提取并比较 +## 4. 逻辑否定:选项 B 和 D 都是"看起来合理但实际错误"的干扰项,模型需要识别出" +变更募集资金用途是普通决议""修改章程是特别决议"这些否定性结论 +## 5. 多选题陷阱:漏选 C 或多选 B/D 都不得分,必须四个选项全部判断准确 +这道题的难度在于:它不是"找到原文就能答对"的简单题,而是需要精准定位多个法条 +→ 提取关键条件 → 逐项逻辑判断 → 综合得出多选组合的复杂推理链。如果检索模块 +漏掉了《章程指引》第八十一条和第八十二条的区分,或者模型把"普通决议"和"特别 +决议"搞混,答案就直接错了。 + +### 表格 1 + +| 选项 | 关键法条定位 | 推理 | +| --- | --- | --- | +| A | 《章程指引》第四十七条第(四)款:"
为资产负债率超过百分之七十的担保对
象提供的担保"须经股东会审议通过 | 被担保方资产负债率 75% >
70%,触发法定审议条件,A 正
确 | +| B | 《章程指引》第八十一条(普通决议事
项)、第八十二条(特别决议事项) | 变更募集资金用途属于普通决议
事项,未列入特别决议清单,B
错误 | +| C | 《治理准则》第四十一条:独立董事不
得与上市公司存在"直接或间接利害关系
" | 候选人所在律所过去 12 个月提
供过法律服务,构成利害关系,
C 正确 | +| D | 《章程指引》第八十二条第(三)款:"
本章程的修改"须经特别决议通过 | 将"对外财务资助"纳入特别决议
范围属于修改章程,必须经特别
决议,D 错误 | + +--- + +## 第 7 页 + +## 2. PDF 解析方案 +RAG 系统的质量上限由两个因素决定:检索策略和文档解析质量。Baseline 教程第七 +章已经指出了 PDF 解析质量是第一个瓶颈,但只给了方向没有展开。这一章详细对比 +主流方案,并解释 baseline 的选择逻辑。 +### 2.1 为什么 PDF 解析是第一步且最重要 +整个 pipeline 的链路是:PDF → 解析 → 文本 → 切片 → 检索 → LLM 回答。如果第 +一步就把表格变成了乱码、公式丢失了、段落顺序乱了,后面所有环节都在错误的文 +本上工作。这种错误无法通过优化检索或 prompt 来补救。 +保险条款和财报类 PDF 有几个让解析器头疼的特点: +- 复杂表格:保费表、现金价值表、赔付比例表,行列密集且嵌套层级深 +- 数学公式:精算公式、财务比率公式,上下标和分式结构容易丢失 +- 双栏/多栏布局:部分研报和合同使用多栏排版,阅读顺序容易错 +- 页眉页脚混入正文:页码、章节标题、公司名称等重复内容污染正文 +- 扫描件 vs 电子件:部分文档是扫描图片(需要 OCR), 部分是原 PD生F(可直 +接提取文字),两者需要不同的处理策略 +### 2.2 主流方案对比 +(1)轻量级:PyMuPDF4LLM(当前 baseline 方案) +PyMuPDF4LLM 是基于 PyMuPDF(原 fitz)的轻量封装,专门为 LLM 场景设计。它 +的核心能力是把 PDF 直接转为 Markdown 文本,保留标题层级(通过字号和字体推 +断),提取表格为 Markdown table 格式。 +优势: +- 纯 Python 库,安装简单(pip install pymupdf4llm) +- 不需要 GPU,纯 CPU 运行 +- 速度快,一份 50 页 PDF 约 5-10 秒完成 +- 对原生 PDF(非扫描件)的文字提取质量尚可 +- 输出 Markdown 格式,直接兼容下游处理 +局限: +- 不包含 OCR 能力,扫描件 PDF 无法处理 +- 表格提取基于规则,复杂嵌套表格容易错位 + +--- + +## 第 8 页 + +- 公式识别能力弱,数学公式可能丢失结构 +- 双栏文档的阅读顺序可能出错 +- 字号推断标题层级不总是准确(比如正文小标题和普通段落字号相同) +为什么 baseline 选它: 赛方评测环境不提供 GPU,PyMuPDF4LLM 是当时能在 +CPU 上快速跑通的方案。17% 的准确率中,有一部分损失确实来自 PDF 解析不够好, +但 baseline 的目标是先把端到端流程跑通。 +(2)中量级:Marker +Marker(VikParuchuri, 2024, 持续更新至 2025 年 1.4.0 版本,GitHub: +VikParuchuri/marker)[1] 是一个基于 pipeline 架构的 PDF 转 Markdown 工具。它的 +流水线是:Surya 布局检测 → Surya OCR(可选)→ 文本提取 → Markdown 输出。 +核心能力: +- 布局检测使用 Surya 模型,能识别标题、正文、表格、公式、图片、页眉页脚等 +区域 +- 内置 OCR 能力,支持 90+ 种语言 +- 对扫描件和原生 PDF 都能处理 +- 输出格式丰富:Markdown、JSON(含位置信息) +- 支持 GPU 加速,也能纯 CPU 运行(速度较慢) +优势: +- 表格和公式处理优于纯规则方案 +- 多语言支持好,中文文档识别质量不错 +- 自动过滤页眉页脚 +- 社区活跃,更新频繁 +局限: +- 依赖 PyTorch 模型,需要 4-8GB 显存(GPU 模式) +- CPU 模式下速度较慢,一份 50 页 PDF 可能需要 5-15 分钟 +- 对极端复杂的表格(多层嵌套、合并单元格)仍有遗漏 +- 公式识别不支持 LaTeX 输出,公式以图片或文本形式保留 +对本赛题的适用性: 如果赛方提供 GPU 环境,Marker 是一个性价比不错的选择。 +CPU 模式下虽然慢,但解析在预处理阶段完成,不占用提交时的 Token 预算,时间成 +本可以接受。 + +--- + +## 第 9 页 + +(3)重量级:MinerU +MinerU(上海 AI Lab / OpenDataLab, 2024 年发布, 持续更新至 2026 年 4 月 3.1.0 +版本,GitHub: opendatalab/MinerU)[2] 是目前开源社区最受关注的 PDF 解析工具之 +一,专为 LLM / RAG / Agent 场景设计。 +核心能力: +- 完整的版面分析管线:布局检测 → 阅读顺序恢复 → 元素识别 → 结构化输出 +- 表格和公式提取质量高,公式自动转为 LaTeX +- 支持 109 种 OCR 语言 +- 输出 Markdown / JSON / HTML / LaTeX 多种格式 +- 内置 MCP Server 支持,可直接集成到 Agent 系统 +- 兼容 LangChain、LlamaIndex、Dify、RAGFlow 等主流 RAG 框架 +优势: +- 公式转 LaTeX 是本赛题的关键优势——保险条款和财报中的数学公式能完整保留 +- 阅读顺序恢复能力强,双栏/多栏文档不乱序 +- 对中文文档优化好(上海 AI Lab 团队中文背景) +- OmniDocBench 评测中英文文档解析表现领先 [3] +- 同时支持本地部署、在线 API、CLI 和 Python SDK +局限: +- GPU 依赖:需要 CUDA 支持的 GPU(推荐 8GB+ 显存),纯 CPU 模式可以运行 +但非常慢 +- 安装复杂:依赖较多系统库,首次配置可能需要 30 分钟以上 +- 内存占用大:处理高分辨率文档时可能占用 10GB+ 内存 +- 单页处理时间:GPU 下约 3-8 秒/页,CPU 下可能 30-60 秒/页 +对本赛题的适用性: 如果赛方允许使用 GPU 环境,MinerU 是目前的最佳选择。公式 +LaTeX 化、表格结构化、阅读顺序恢复这三项能力直接解决了本赛题 PDF 解析的核心 +痛点。即使赛方只给 CPU 环境,MinerU 也可以跑(只是慢),预处理阶段的时间成本 +通常可以接受。建议关注赛方最终公布的环境配置。 +补充说明:Meta 在 2023 年发布的 Nougat 是最早用端到端 Transformer 做 PDF 公式 +解析的工作之一,MinerU 的公式处理模块 UniMERNet 吸收了其思路并做了改进。 +Nougat 作为独立工具已不再活跃维护,且专为英文学术论文设计,不适用于本赛题的 +金融文档,此处不再展开。 + +--- + +## 第 10 页 + +(4)视觉语言模型级:olmOCR +olmOCR(Allen AI, 2025 年 2 月发布, arXiv:2502.18443)[4] 代表了 PDF 解析的新 +方向:不用传统的 OCR + 版面分析流水线,而是用一个精调的 7B 视觉语言模型 +(VLM)直接把 PDF 页面转成结构化的纯文本。 +核心能力: +- 基于 7B VLM,精调在 260,000 页多样化 PDF 上 +- 支持表格、列表、公式、手写文字的提取 +- 输出线性化的纯文本,保持自然阅读顺序 +- 批处理效率高,100 万页仅需 176 美元(使用开源模型时几乎免费) +- 在 olmOCR-Bench 上表现优于 GPT-4o、Gemini Flash 2 和 Qwen-2.5-VL +优势: +- 不需要复杂的版面分析管线,一个模型搞定所有页面类型 +- 对扫描件、手写文档、低质量 PDF 的处理能力远超传统 OCR 方案 +- 开源的 7B 模型可以在本地部署,不需要调用付费 API +局限: +- 需要 GPU(7B VLM 推理),CPU 不可用 +- 中文文档的处理质量未知(训练数据以英文为主) +- 输出格式为纯文本而非 Markdown,丢失了部分结构信息 +- 表格和公式以文本形式呈现,不如 LaTeX 精确 +对本赛题的适用性: 如果赛方提供 GPU 环境且中文处理质量过关,olmOCR 是一个 +值得尝试的方案。但目前中文支持情况不明朗,建议等社区有中文评测结果后再决定。 + +--- + +## 第 11 页 + +### 2.3 本赛题的 PDF 解析选型建议 +根据赛方可能提供的环境,分三种情况: +即使赛方最终只给 CPU 环境,学习 MinerU 和 olmOCR 也是有价值的。 了解更好的 +解析方案能帮助理解当前 baseline 的瓶颈在哪里(哪些错误是 PDF 解析导致的,哪 +些是检索策略导致的,哪些是 prompt 设计导致的)。定位清楚瓶颈,才能精准优化。 +### 2.4 预处理阶段可以做的工作 +无论用哪个解析工具,以下预处理手段对提升文本质量有帮助: +## 1. 页眉页脚过滤:用正则匹配重复出现的页码、章节标题、公司名称等,统一去除 +## 2. 表格后处理:对解析出的表格做格式校验(行列数一致、数值列对齐),异常时回 +退到手动提取 +## 3. 公式补标注:在公式附近插入描述性文本(如"该公式表示:现金价值 = 累计保费 +× 75%"), 帮助LLM 理解公式含义 +## 4. 段落合并:同一段落的文本可能被解析成多行,按标点和语义做合并 +## 5. 文档结构标注:为每个段落标注所属章节路径(如"第二章 > 第三节 > 现金价值计 +算"),增强检索时的上下文信息 + +### 表格 1 + +| 环境 | 推荐方案 | 理由 | +| --- | --- | --- | +| 纯 CPU | PyMuPDF4LLM +
预处理优化 | 无 GPU 时的唯一选择。可以在预处理阶
段做更多工作来补偿解析质量(如人工修
正表格、补充公式描述) | +| GPU(8GB+) | MinerU | 当前开源方案中综合能力最强,公式/表格/
阅读顺序三项核心能力突出 | +| GPU(16GB+) | MinerU +
olmOCR 互补 | MinerU 处理结构化文档,olmOCR 处理
扫描件和低质量页面,两路互补 | + +--- + +## 第 12 页 + +## 3. RAG 检索优化 +Baseline 的硬截断方案本质上跳过了检索,直接把文档前半部分塞给模型。要突破 17% +的准确率天花板,第一步就是引入检索模块,但必须注意检索引入的连贯性断裂风险。 +### 3.1 分块策略 +文档切片是 RAG 系统的地基。切得不好,后面的检索和生成都受影响。 +固定大小分块:按固定字符数或 token 数切分,实现简单但容易切断语义。比如"因意 +外伤害发生上述情形的,无等待期"这句话如果被切成两块,前半句在一块的末尾、后 +半句在另一块的开头,检索可能只命中其中一块,模型看到的就不完整。 +滑动窗口分块:相邻块之间有重叠(通常 10%-20%), 减少边界信息丢失。 适合保险 +条款这种"前后文紧密关联"的文档。重叠太多会导致检索结果重复,占用 token 预算。 +语义分块:按段落、章节等自然边界切分,保持每个块内的语义完整。Markdown 文 +档天然适合这种策略(按标题层级切分)。缺点是块大小不均匀,有的块可能过长。 +多粒度分块(Small-to-Big / Parent-Child):这是更进阶的策略。把文档切成两层: +- 子块(Child Chunks):较小(200-500 token),用于向量检索。 小块嵌入更聚焦 , +检索精度更高。 +- 父块(Parent Chunks):较大(800-2000 token), 包含子块的完整上下文。 检 +索命中子块后,把对应的父块也一并取出来送给 LLM。 +这种做法的好处是"检索用小块(精确)、回答用大块(完整)"。实验表明,子块检索 ++ 父块上下文的方案在准确率上比单一大块方案高 10%-15%。 +推荐起步方案:按 Markdown 的 ## 标题切分 + 滑动窗口,块大小控制在 800-1200 +token,重叠 15%。有精力的话,升级到多粒度分块(子块 400 token 检索 + 父块 +1200 token 回答)。 +### 3.2 混合检索 +单一检索策略各有盲区: +- BM25 对精确匹配敏感(数字、条款编号),但无法理解"退保费用"和"退保手续费" +是同一个东西 +- 向量检索(Embedding)能捕捉语义相似性,但对数字和年份不够敏感 +混合检索 = BM25 + 向量检索,两路同时召回,结果融合后一起送进 LLM。融合方式: +- 简单合并去重:两路结果拼接,去重后按原始文档顺序排列 +- RRF(Reciprocal Rank Fusion):按每路结果的排名位置加权融合,不依赖绝 + +--- + +## 第 13 页 + +对分数。公式:score(d) = Σ 1/(k + rank_i(d)),k 通常取 60 +- 加权分数融合:两路结果归一化后按权重加和(如 BM25 权重 0.3 + 向量 0.7), +需要调参 +实现要点: +- BM25 用 rank-bm25 库,分词用 jieba +- 向量检索可选 text-embedding-3-small(百炼提供)或本地模型 bge-large- +zh +- 两路各召回 20-30 个片段,融合后取 top 10-15 + +--- + +## 第 14 页 + +BGE 模型系列速览(扩展学习) +注:经向赛题方确认,Embedding 模型和 Rerank 模型不允许在正式提交中使用。以 +下内容仅供扩展学习,帮助理解 RAG 系统的完整技术栈,不建议在正式方案中依赖。 +BAAI(北京智源人工智能研究院)开源的 BGE 系列是目前中文 RAG 场景最常用的模 +型族,覆盖了 Embedding 和 Reranker 两类需求。了解各型号的差异,有助于根据实 +际算力条件选型: +Embedding 模型(向量检索): +Reranker 模型(重排序): +对本赛题的建议:如果赛方给 GPU,bge-m3(Embedding)+ bge-reranker-v2-m3 +(Reranker)是多语言场景的稳妥组合;如果只有 CPU,bge-large-zh-v1.5 + +bge-reranker-base 可以在可接受的速度下完成检索和重排。 + +### 表格 1 + +| 模型 | 参数量 | 特点 | 适用场景 | +| --- | --- | --- | --- | +| bge-large-zh-
v1.5 | 326M | 中文专用,C-
MTEB 榜单前列 | 纯中文文档,CPU
可跑但推荐 GPU | +| bge-m3 | 568M | 多语言,支持
8192 token 长文
本,可同时输出
dense + sparse 向
量 | 中英混合文档,或
需要长上下文嵌入 | +| bge-
multilingual-
gemma2 | 9B | 最新一代,MTEB
多语言榜首,精度
最高 | GPU 充足时的最
强选择 | + +### 表格 2 + +| 模型 | 参数量 | 特点 | 适用场景 | +| --- | --- | --- | --- | +| bge-reranker-
base | 278M | 轻量,CPU 可
跑,中文效果不错 | 算力受限时的默认
选择 | +| bge-reranker-
large | 560M | 精度高于 base,
需要 GPU | 有 GPU 时的升级
选择 | +| bge-reranker-
v2-m3 | 568M | 多语言,与 bge-
m3 配套使用 | 中英混合文档的重
排序 | + +--- + +## 第 15 页 + +视觉检索:跳过 OCR 的另一种思路 +前面的所有方案都遵循同一个范式:PDF → 解析为文本 → 切片 → 文本检索。但如果 +PDF 解析本身就是瓶颈(表格乱码、公式丢失、双栏错序),有没有可能直接跳过解 +析? +ColPali(2024)和 ColQwen(2025)[15] 提出了视觉检索的思路:把 PDF 页面渲 +染成图像,用视觉语言模型(VLM)直接从页面图像中提取 patch 级别的嵌入,然后 +基于这些视觉嵌入做检索。整个过程不需要 OCR、不需要版面分析、不需要 +Markdown 转换。 +具体做法: +## 1. 把每页 PDF 渲染为高分辨率图像(如 448×448) +## 2. VLM 对图像做 patch 编码(如 ColPali 产出每页 1024 个 patch embedding) +## 3. 查询时,问题文本和页面图像的 patch embedding 做 MaxSim 匹配(取每个 +query token 与所有 patch 的最大相似度之和) +## 4. 返回最相关的页面 +优势: +- 完全绕过了 PDF 解析的所有问题:表格、公式、双栏都不需要处理 +- 布局信息天然保留(表格的视觉结构、公式的排版位置) +- ViDoRe 评测基准上,ColPali 的检索精度超越了传统 OCR + 文本检索方案 +局限: +- 需要 VLM(3B+ 参数),GPU 必须 +- 返回粒度是整页,不是精确的文本片段:模型需要自己从页面图像中"读"出答案 +- 目前以英文文档为主,中文文档的视觉检索效果待验证 +- ColModernVBERT 提供了一个 250M 参数的轻量替代,精度接近 ColPali 但模型 +小 10 倍 +对本赛题的启示: 视觉检索提供了一个激进的思路:如果 MinerU 或 Marker 的解析效 +果在特定文档上仍然不理想,可以直接把页面当图片检索,搭配多模态 LLM(如 +GPT-4o、Qwen-VL)来"看图答题"。这在保险条款的复杂表格和公式场景下可能有奇 +效。但 GPU 要求和中文适配是两个现实障碍,建议作为实验性方向而非主方案。 +### 3.3 重排序 +初步检索的结果中可能混入弱相关片段。重排序用更精细的模型对候选片段逐一打分, +把真正相关的排到前面。 + +--- + +## 第 16 页 + +常见方案: +- Cross-Encoder 模型:把"问题 + 候选片段"拼接后送入模型,直接输出相关性分数。 +BGE Reranker 系列的具体型号选择见上节速览表。 +- LLM-based Rerank:用 LLM 自己对候选片段打分。优点是灵活(可以按自定义 +标准评分),缺点是慢且消耗 token。 +建议:如果使用百炼 Embedding API,可以搭配百炼的 Rerank API;如果本地部署, +见上节 BGE 速览表按算力条件选择。 +### 3.4 查询增强 +查询增强策略(如 HyDE、查询改写、查询分解)旨在缩小用户口语化提问与文档专 +业术语之间的语义差距。具体方案见 3.6 节"前沿 RAG 范式"中的 HyDE 讨论。 +### 3.5 片段组装 +检索到的片段不能直接拼接就送给 LLM。需要注意: +- 去重:滑动窗口会导致相邻片段内容重叠,需要去重 +- 排序:按文档原始顺序排列片段(而不是按相关性分数),保持原文的逻辑流 +- 截断:总 prompt 长度受 token 预算限制,需要在片段数量和质量之间做取舍 +### 3.6 前沿 RAG 范式 +前面讲的分块、混合检索、重排序都属于"标准 RAG"的优化手段,在分块质量、检索 +精度、排序准确性上做改进。近一年来,RAG 领域出现了几个重要的新范式,它们不 +是调参数,而是改变了检索的底层结构:从"搜碎片"升级到"理解结构"。 +理解这些范式的关键,在于搞清楚它们各自在解决什么问题。传统 RAG 有三个根本缺 +陷: +## 1. 扁平化:所有文档块在同一层,检索只看块级别的相似度,无法理解跨块的语义 +结构(一个公式分散在三段里,单块检索无法还原) +## 2. 局部性:擅长回答"这段话说了什么",但回答不了"这些文档整体讲了什么"(四份 +保险条款的退保规则有什么系统性差异?) +## 3. 无反思:检索到什么就用什么,不评估质量,不修正方向 +下面的范式各自针对其中一个或多个缺陷。 + +--- + +## 第 17 页 + +GraphRAG:用知识图谱做检索 +GraphRAG(Microsoft Research, 2024 年 4 月发布, arXiv:2404.16130)[5] 的核心思 +路:不要只把文档切成碎片去搜,而是先从文档中提取实体和关系,构建知识图谱, +再基于图谱做检索和推理。 +传统 RAG 本质上在做"向量空间中的邻居搜索":把问题和文档块都映射到向量空间, +找距离最近的块。这种做法的上限是"找相似的文本片段",但无法回答需要跨片段推理 +的问题。比如"这四份保险条款中,哪份对意外身故的赔付最高?",这个问题需要先分 +别理解四份条款的身故保险金计算规则,再横向比较,而不是找到某个"相似"的片段就 +够了。 +GraphRAG 的完整流程: +## 1. 实体与关系抽取:用 LLM 从源文档中提取实体和关系。实体可以是产品名("平 +安智盈金生")、概念 "(现金价值")、数值("75%")、条款编号"第( 7 保单年度");关 +系描述实体之间的逻辑连接("现金价值 → 计算公式 → 累计保费 × 75%"、"意外身故 +→ 适用规则 → 无等待期")。 +## 2. 知识图谱构建:将抽取出的实体作为节点、关系作为边,构建一张有向/无向图。 +这张图不是简单的关键词共现网络,而是携带了语义信息的知识结构。 +## 3. 社区检测:用 Leiden 算法在图结构上发现紧密关联的实体群组(社区)。每个社 +区对应一个主题领域,比如"退保规则社区"可能包含"退保费用""退保费用率""现金价值 +""保单年度"等实体及其关系。 +## 4. 社区摘要生成:对每个检测出的社区,用 LLM 生成一个总结性的摘要,描述这个 +社区的核心信息。这一步是 GraphRAG 区别于传统图方法的关键:不是把原始文本片 +段直接存到图里,而是对每个主题生成高层摘要,使得检索时可以直接读取"关于退保 +规则的核心要点是什么"。 +## 5. 两种检索模式: +○ Local Search(局部搜索):查询时匹配相关实体,从实体出发沿图的边扩散 +到邻居节点,获取与问题直接相关的实体和关系。适合事实查找类问题("平安智 +盈金生的现金价值怎么算?")。 +○ Global Search(全局搜索):查询时匹配相关社区,用社区的摘要生成部分 +回答,再将所有部分回答汇总成最终答案。适合全局理解类问题("四份条款的退 +保规则有什么系统性差异?")。这一步本质上是 Map-Reduce:社区摘要做 Map, +LLM 汇总做 Reduce。 +对本赛题的适用性: +跨文档对比题和多文档联合推理题是 GraphRAG 的最优场景。比如 ins_a_001(四产 +品身故保险金排序),传统 RAG 需要逐份文档检索每份产品的身故保险金规则,再在 + +--- + +## 第 18 页 + +prompt 里手动对比。GraphRAG 可以在知识图谱中直接查询"身故保险金"这个实体, +沿关系边找到四个产品的计算规则,甚至社区摘要已经预先生成了"四产品身故保险金 +对比"的总结。 +代价是预处理阶段需要大量 LLM 调用:实体抽取(每份文档)、社区摘要(每个社区)。 +对于本赛题的 100 题对应的文档集,这个开销是可控的,大约几十到几百次 LLM 调用, +可以在提交前离线完成,不占用正式提交的 Token 预算。 +实现参考: 微软开源了 graphrag Python 包,支持本地运行。对于本赛题的中文金融 +文档,需要替换内置的实体抽取 prompt 为中文版本。 +LightRAG:轻量级图 RAG +LightRAG(香港大学 HKUDS 实验室, 2024 年 10 月发布, EMNLP 2025, +arXiv:2410.05779)[6] 是 GraphRAG 的轻量级替代方案,保留了图结构的优势,但大 +幅降低了构建成本和检索延迟。 +与 Microsoft GraphRAG 的关键差异: +GraphRAG 的社区检测 + 逐社区摘要是一个重操作:Leiden 算法需要遍历整张图,每 +个社区的摘要又需要独立的 LLM 调用。LightRAG 不依赖社区检测,而是用双向量索 +引来同时支持局部和全局检索: +## 1. 实体向量索引:每个实体节点有独立的向量表示(由实体名称和描述的 +embedding 组成)。查询时,问题向量与实体向量做相似度匹配,找到相关实体后沿关 +系边扩散到邻居节点。这对应 GraphRAG 的 Local Search。 +## 2. 关系向量索引:每条关系边也有独立的向量表示(由关系描述和两端实体信息的 +embedding 组成)。查询时,问题向量与关系向量做相似度匹配,直接找到相关的关系。 +这能捕捉到跨实体的语义模式(比如"退保费用率逐年递减"这种趋势性关系),而不仅 +仅是单个实体的匹配。 +## 3. 关键词引导查询:LightRAG 从用户问题中提取关键词,将这些关键词的向量拼接 +成组合查询向量。这种方式比直接用整个问题做向量检索更聚焦,减少了语义稀释。 +增量更新能力: +LightRAG 的图结构支持增量插入——新文档加入时,只需要提取新文档的实体和关系、 +计算它们的向量、插入图结构中,不需要重建已有索引。这对本赛题可能很重要:A +榜只有 100 题对应的一批文档,如果 B 榜下发新文档,LightRAG 可以直接增量更新, +而 GraphRAG 需要重新跑社区检测。 +对本赛题的定位: +LightRAG 适合作为从标准 RAG 向图 RAG 过渡的第一个实验方案。它的构建成本接 +近传统 RAG(主要开销在实体抽取,不需要社区检测),但能同时支持局部事实查找 +和全局对比推理。如果本赛题后续出现需要跨文档联合推理的难题,LightRAG 的性价 + +--- + +## 第 19 页 + +比优于全量 GraphRAG。 +实现参考: 开源项目 HKUDS/LightRAG 提供了完整的 Python 实现。 +StructRAG:按任务类型选择知识结构 +StructRAG(2024 年发布)[7] 提出了一个有意思的视角:不同任务需要不同的知识 +组织方式,不应该统一处理。 +它定义了五种结构类型,根据任务特征自动选择最合适的结构: +对本赛题的启示:不同题型应该用不同的知识组织方式。事实查找题用片段检索就够 +了;计算题需要从文档中提取公式构建成"算法"结构;跨文档对比题需要图结构。一个 +统一的检索框架可能不如按题型分派检索策略来得有效。 + +### 表格 1 + +| 结构类型 | 适用任务 | 组织方式 | +| --- | --- | --- | +| 表格(Table) | 统计类任务 | 结构化行列 | +| 图(Graph) | 长链推理任务 | 实体-关系网络 | +| 算法(Algorithm) | 规划类任务 | 步骤流程 | +| 目录(Catalog) | 总结类任务 | 层级分类 | +| 片段(Chunk) | 简单单跳问答 | 原始文本块 | + +--- + +## 第 20 页 + +RAPTOR:树状递归摘要检索 +RAPTOR(Recursive Abstractive Processing for Tree-Organized Retrieval) +(2024 年 1 月发布, arXiv:2401.18059)[8] 解决的是"扁平检索丢失全局上下文"的问 +题,思路和 GraphRAG 不同:GraphRAG 用图结构,RAPTOR 用树结构。 +为什么需要树结构: +传统 RAG 把所有文档块平铺在一层。检索时只看单个块的相似度,但有些问题的答案 +跨越了多个块。一个典型的保险条款场景:某产品的退保规则可能分布在"第二章现金 +价值"(定义公式)、"第五章退保"(退保流程)、"附录"(费率表)三个不同章节。这 +三个章节在文档中相距很远,但在语义上紧密关联。扁平检索很难同时召回这三个章 +节的片段。 +RAPTOR 的构建流程: +## 1. 叶子层:将文档切成基础块(如每页 Markdown),每个块做embedding +## 2. 聚类:用高斯混合模型(GMM)对叶子节点的 embedding 做软聚类,每个节点 +可以属于多个聚类 +## 3. 摘要:对每个聚类,用 LLM 对聚类内的所有文本块生成一个摘要。这个摘要成为 +上一层的一个节点 +## 4. 递归:对上层节点重复聚类 + 摘要,直到只剩一个根节点 +## 5. 最终结构:一棵多层树,底层是原始文本块(精确细节),中层是主题摘要(如" +退保规则""身故保险金规则"), 顶层是全局摘"该要产(品如的核心条款概述") +检索时的层级选择: +- 简单事实查找("平安智盈金生第 7 年的现金价值是多少?")→ 从叶子层检索, +直接拿精确数值 +- 中等复杂度("该产品的退保规则有哪些注意事项?")→ 从中层摘要节点检索, +获取主题级别的完整信息 +- 全局理解("该产品的整体保障结构是怎样的?")→ 从顶层摘要节点检索 +变体与改进: +- adRAP(Adaptive RAPTOR):解决 RAPTOR 在动态数据集上的问题。原始 +RAPTOR 每次新增文档都需要重建整棵树(因为聚类会变)。adRAP 通过增量调整聚 +类结构来避免全量重建,适合文档持续增加的场景。 +- postQFRAP:不改变检索端的索引结构,而是在检索结果回来之后,对召回的片 +段做一次"查询聚焦的递归摘要处理":把检索结果按 RAPTOR 的思路聚类+摘要,生 +成一个结构化的上下文再送给 LLM。优势是可以作为后处理模块接入任何检索管线。 +对本赛题的适用场景: + +--- + +## 第 21 页 + +行业研报类题目(如 res_a_001 银保渠道发展历程)和跨年度对比题(如 fin_a_001 +比亚迪两年年报对比)是 RAPTOR 的优势场景:这些题需要同时理解宏观趋势和微观 +数据,树结构天然匹配"总-分"的信息粒度。 +Self-RAG 与 CRAG:让模型反思检索质量 +传统 RAG 无条件信任检索结果:搜到什么就用什么,不管质量如何。这在实践中是个 +严重问题:BM25 可能因为关键词匹配把不相关的片段排到前面,向量检索可能因为 +语义模糊召回弱相关的内容。Self-RAG 和 CRAG 在这里加了"自我评估"机制。 +Self-RAG(2024 年 10 月发布, arXiv:2310.11511)[9] 的核心是"检索 + 反思 + 重试" +循环: +## 1. 按需检索:不是每个问题都需要检索。Self-RAG 先判断当前问题是否需要检索 +(简单常识问题可能不需要) +## 2. 检索文档片段 +## 3. 对每个片段做三方面评估:相关性(这个片段和问题相关吗?)、支持度(这个片 +段支持还是反对某个观点?)、有用性(这个片段对回答有帮助吗?) +## 4. 对低质量片段重新检索或修正 +## 5. 生成最终答案时,标注每个论断的来源片段和可信度 +CRAG(Corrective RAG)(2024 年 1 月发布, arXiv:2401.15884)[10] 在 Self-RAG +基础上更进一步,加入了自动纠错机制: +## 1. 检索文档片段 +## 2. 用检索质量评估器对整体检索结果打分(0-1 分) +## 3. 根据分数分三级处理: +○ 分数 ≥ 0.7(CORRECT): 检索质量好 ,直接使用 +○ 分数 0.3-0.7(AMBIGUOUS): 质量一般 ,保留当前结果同时用不同关键词 +补充检索,两轮结果合并 +○ 分数 ≤ 0.3(INCORRECT): 质量差 , 丢弃检索结果 , 切换到全文阅读或其 +他策略 +## 4. 最终生成前再做一次"幻觉检查",过滤掉检索结果中不存在的论断 +CRAG 的关键洞察: 检索质量的评估不需要复杂的模型。用检索结果中每个片段与问 +题的关键词重叠度、片段间的语义一致性等轻量特征,就能大致判断检索质量。只有 +当评分落在中间区域时才需要 LLM 介入做进一步判断。 +对本赛题的适配: + +--- + +## 第 22 页 + +本赛题不允许网络搜索,但 CRAG 的三级处理思路可以直接适配: +- CORRECT → 直接用检索结果生成答案 +- AMBIGUOUS → 换关键词重新检索同一文档集,或者扩大检索范围(如从只搜前 +10 个块扩展到搜全部块) +- INCORRECT → 放弃检索,改为读全文(回到 baseline 的硬截断方案作为 +fallback) +这个机制的价值在于:它让系统在"检索结果好"的时候享受检索的效率,在"检索结果 +差"的时候自动回退到更稳健的方案,而不是一味信任检索。 +HyDE:用假设答案做检索 +HyDE(Hypothetical Document Embeddings)(2023 年 12 月发布, +arXiv:2212.10496)[11] 解决的是"用户问题用词和文档用词不一致"导致检索失败的问 +题。 +问题场景: 用户问"怎么退保最划算",文档里写的是"现金价值计算方式"和"退保费用 +率表"。问题中的"划算"是口语化表达,文档中的"现金价值"是专业术语,两者在向量 +空间中距离很远,直接向量检索可能召回到完全不相关的内容。 +HyDE 的做法: 先让 LLM 凭空生成一份假想的"理想答案",再把这份假想答案(而非 +原始问题)转成向量去检索。假想答案是用文档风格写的(包含"现金价值""退保费用 +""已交保费"等专业术语),所以更容易匹配到真实的相关片段。 +流程:用户问题 → LLM 生成假设答案 → 假设答案 Embedding → 向量检索 → 返回 +真实片段。 +代价是每次检索多一次 LLM 调用(生成假设答案),但检索命中率提升显著:在多个 +benchmark 上,HyDE 的召回率比直接向量检索高 10%-20%。对本赛题的口语化问题 +(如"哪个产品最划算")特别有效。 +HyDE + 混合检索的实践建议: 根据"Searching for Best Practices in RAG"(2024 年 +7 月发布, arXiv:2407.01219)[12] 的系统评测,HyDE + 混合检索(BM25 + 向量) +是目前综合表现最好的默认配置。该评测在多个 benchmark 上对比了不同检索策略, +HyDE + Hybrid Search 的检索质量显著优于单独的查询改写(Query Rewriting)或查 +询分解(Query Decomposition)。 +KAG:专业领域知识增强 +KAG(Knowledge Augmented Generation)(2024 年发布)[14] 针对的是专业领 +域 RAG 的一个特殊问题:通用知识图谱的实体和关系定义与专业领域不匹配。 +例如,用通用 OpenIE 工具从保险条款中抽取实体,可能把"现金价值"标注为普通名词 +而非专业概念,把"第 7 保单年度"拆成两个无关实体。KAG 的做法是引入领域知识对 + +--- + +## 第 23 页 + +齐:用领域概念体系(如保险术语表)来指导实体抽取和关系定义,减少信息抽取过 +程中的噪音。 +对本赛题的启示:如果使用 GraphRAG/LightRAG 做实体抽取,需要定制实体类型 +(产品名称、条款编号、计算公式、数值、条件判断)和关系类型(属于、计算依据、 +例外条件、时间约束),而不是用通用 prompt。否则抽取出的实体关系图可能充满噪 +音,反而干扰检索。 +多跳推理 RAG(Multi-hop RAG) +多跳推理是指答案不能从单个文档片段直接获得,需要通过多个片段的推理链来推导。 +本赛题中这类问题很常见:"某产品在第 N 年的退保金额是多少?"需要先找到退保公 +式(第一跳),再找到该年度的退保费用率(第二跳),再找到该年度的现金价值(第 +三跳),最后代入计算。 +传统 RAG 对多跳问题的处理很弱:检索可能只召回第一跳的片段(退保公式),但漏 +掉了后两跳(费用率和现金价值)。针对多跳 RAG 的改进方案: +- Iterative RAG(迭代检索):第一轮检索 → 生成部分答案 → 根据部分答案生成 +新查询 → 第二轮检索 → 汇总。每轮检索聚焦一个子问题 +- IRCoT(Interleaving Retrieval with Chain-of-Thought):把思维链推理和检索 +交替进行。每推一步就检索一次,用检索结果来验证和修正推理方向 +- Beam Search RAG:对多跳路径做束搜索,同时探索多条推理链,最后选证据最 +充分的一条 +Agentic RAG:让 Agent 自主编排检索策略 +前面所有的 RAG 范式都预设了检索策略:什么时候搜、搜什么、搜几次,都是提前写 +死的。Agentic RAG 的思路是把这个决策权交给 LLM 自己:通过 Function Calling, +LLM 自主决定: +- 当前问题需要检索吗?(简单常识题可能不需要) +- 搜什么关键词?(可能需要多轮尝试不同关键词) +- 搜到了什么程度才算足够?(自主判断信息是否足够) +- 搜到的东西质量差怎么办?(自动切换策略) +这和本赛题的 Agent 定位高度一致。Baseline 测试过 Function Calling,Token 消耗比 +no-tool 高约 18%,但 FC 的灵活性(模型可以自主决定"什么时候搜、搜什么")是提 +升准确率的关键路径。具体方案见第六章"Agent 化方向"。 + +--- + +## 第 24 页 + +### 3.7 本赛题的 RAG 路线建议 +从 baseline 的硬截断出发,建议按以下顺序逐步引入 RAG 优化: +建议每完成一个阶段就跑一次 A 榜提交,验证实际收益,避免"加了优化反而降分"的 +陷阱。 + +### 表格 1 + +| 阶段 | 方案 | 预期收益 | 风险 | +| --- | --- | --- | --- | +| 1 | BM25 检索 + 语义分
块 | 解决"答案不在前
4000 字"的问题 | 连贯性断裂,可能降分 | +| 2 | + 多粒度分块(子块检
索 + 父块回答) | 缓解连贯性断裂 | 实现复杂度增加 | +| 3 | + 混合检索(BM25 +
向量)+ HyDE | 提升召回率,改善口
语化问题匹配 | 需要 Embedding 模型,
HyDE 增加一次 LLM 调
用 | +| 4 | + Rerank 精排 | 提升精确率 | 额外计算开销 | +| 5 | + 动态记忆压缩(Snip
+ Memento + 结构化
摘要) | 在有限 Token 预算内
保留最多有效信息,
多文档场景收益明显 | 压缩策略调优需要实
验,过度压缩可能丢失
关键条件 | +| 6 | + Bash 工具(Python
代码执行做数值计
算) | 消灭公式计算题的算
术错误 | 需要设计沙箱执行环境 | +| 7 | + CRAG 自检机制
(质量评估 + 自动回
退) | 减少低质量检索导致
的错误 | 需要设计评估器逻辑 | +| 8 | LightRAG(跨文档
题) | 解决跨文档联合推
理,支持增量更新 | 预处理 Token 消耗 | +| 9 | Agentic RAG(FC 自
主编排检索策略) | 灵活应对不同题型 | Token 消耗增加约
18%,需要调优 FC 行
为 | +| 10 | smolagents Code
Agent(Python 代码
编排工具调用) | 减少 30% 推理步
骤,精准数值计算 | 需要学习 smolagents 框
架,代码生成质量依赖
模型能力 | + +--- + +## 第 25 页 + +## 4. 上下文管理与动态记忆压缩 +赛题名称里就有"动态记忆压缩",这其实定义了本赛题的核心技术挑战。前面三章 +(PDF 解析、RAG 检索)解决的是"怎么找到有用信息",这一章解决的是"怎么在有 +限 Token 预算内把最重要的信息喂给模型"。 +### 4.1 问题的本质:上下文窗口是稀缺资源 +每道题的推理链路可以拆成:读文档 → 提取信息 → 判断 → 输出答案。每一步都消耗 +Token,但预算有限(赛方总预算 500 万 Token)。这意味着不能把所有文档全读一遍 , +也不能把检索结果全塞进 prompt。 +上下文管理的核心问题可以表述为:给定 Token 预算 B,如何从候选信息池中选择一 +个子集,使得模型回答正确的概率最大化? +候选信息池包括: +- 文档原文片段(检索结果) +- 上一步推理的中间结果 +- 题目本身和选项 +- Prompt 模板 +- 验证/自检的二次推理结果 +### 4.2 动态截断 +Baseline 的硬截断固定取前 4000 字符。更好的策略是让模型自己决定什么时候停止 +阅读。 +实现思路:分段读取文档,每读一段就让模型判断"当前信息是否足够回答问题"。如果 +足够就停止,不够就继续读下一段。这本质上是 Agent 化的第一步。 +更精细的做法是按题型设定不同的阅读预算。事实查找题可能读前 2000 字符就能找 +到答案;跨文档对比题需要读多份文档,每份可能 1000 字符就够;公式计算题只需 +要定位到公式所在的段落,读到公式后就可以停。 +### 4.3 信息密度控制 +保险条款和监管法规中有大量模板化文本(如"本合同的构成""投保须知"等),这些内 +容对答题几乎没有价值,但占用了大量 token。 +可以预处理时过滤掉高频无意义段落,或者在检索时对"释义""名词定义"等章节降权。 +更系统化的做法是按题型定义信息价值函数:对计算题,公式段落的权重最高;对条 +款定位题,例外条件段落的权重最高。 + +--- + +## 第 26 页 + +### 4.4 动态记忆压缩:分层存储与选择性遗忘 +这才是本赛题"动态记忆压缩"的核心。前面的截断和密度控制都是静态策略,动态压缩 +需要在推理过程中持续做决策:什么该记住、什么该压缩、什么该丢弃。 +工业界的实践:Agent 的记忆分层 +几个主流 Agent 框架都有类似的记忆管理机制,核心思路都是分层存储: +Kimi Code CLI 的 Context Compaction [19] +当对话历史超过 token 阈值时自动触发 compaction,让 LLM 将历史对话压缩为结构 +化摘要,按优先级保留: +## 1. Current Task State(当前任务状态:正在做什么) +## 2. Errors & Solutions(遇到的错误和解决方案) +## 3. Code Evolution(代码最终版本,丢弃中间尝试) +## 4. System Context(项目结构、环境配置) +## 5. Design Decisions(架构决策及理由) +## 6. TODO Items(未完成的任务) +Compaction 前后有 PreCompact / PostCompact hooks 可插拔,允许用户自定义压缩 +策略(如阻止特定信息的压缩)。压缩后的历史替换原始对话,模型只看到摘要。 +这个机制对本赛题的启发:压缩的核心是有选择地保留、改写、丢弃。Kimi Code 的 6 +级优先级给出了一个信息价值排序的框架。对于本赛题,我们可以定义类似的优先级: +## 1. 题目的完整文本和所有选项(最高优先级,绝不压缩) +## 2. 从文档中提取出的公式、数值、条件判断(核心证据) +## 3. 检索到的原文片段中与题目直接相关的部分 +## 4. 文档章节标题和结构信息(用于定位和引用) +## 5. 中间推理过程的摘要 +## 6. 检索到的原文中经判断为不相关的部分(最低优先级,优先丢弃) + +--- + +## 第 27 页 + +Hermes 的 Trajectory Compressor [20] +Hermes 是面向通用 Agent 任务的框架,其 trajectory_compressor.py 实现了一套 +完整的对话历史压缩管线,虽然是为训练数据压缩设计的,但核心算法可以直接迁移 +到推理场景。 +压缩算法的完整流程: +第一步:计算压缩需求。 遍历 trajectory 中每个 turn,用 tokenizer 计数。如果总 +tokens 未超过目标上限(默认 15250),直接跳过压缩。 +第二步:确定受保护区域。 在 trajectory 中标记不可压缩的 turns: +- 头部保护:第一个 system 消息、第一个 human 消息、第一个 gpt 回复、第一个 +tool 调用。这些是任务的初始上下文,丢弃后模型会丢失任务定义。 +- 尾部保护:最后 N 个 turns(默认 N=4)。这些包含最新的推理状态和即将做出的 +决策。 +第三步:计算压缩区域。 受保护头部之后、受保护尾部之前的所有 turns 构成"可压缩 +区域"。从这个区域的起始位置开始,逐 turn 累积 tokens,直到累积量满足: +Plaintext +累计 tokens ≥ (总 tokens - 目标上限) + 摘要目标 tokens +如果遍历完整个可压缩区域仍不满足,则压缩整个区域。 +第四步:LLM 生成摘要。 将被压缩的 turns 拼接为一段文本,发送给单独的摘要模型 +(默认 gemini-3-flash,temperature=0.3)。 摘要prompt 明确要求包含: +- 助手执行了哪些操作(工具调用、搜索、文件操作) +- 获得了什么关键信息或结果 +- 做出了什么重要决策或发现 +- 相关的数据、文件名、数值、输出 +摘要以 [CONTEXT SUMMARY]: 为前缀,目标长度 750 tokens。 +第五步:组装压缩后的 trajectory。 头部 turns(原文保留)+ 摘要消息(作为 +human 消息插入)+ 尾部 turns(原文保留)。如果 system 消息中配置了 +add_summary_notice,会在 system prompt 末尾追加提示文字,告知模型部分历史 +已被压缩。 +压缩效果度量: 每条 trajectory 压缩后记录详细 metrics: +- compression_ratio:压缩后 tokens / 原始 tokens +- tokens_saved:节省的 tokens 数 + +--- + +## 第 28 页 + +- turns_removed:被移除的 turns 数 +- still_over_limit:压缩后是否仍超预算 +对本赛题的关键启发: +Hermes 压缩器给了一个精确的算法框架:在 token 预算约束下,保护首尾、压缩中 +间、LLM 替代。迁移到本赛题的单题推理场景: +- "首"是什么? 题目文本 + 选项(不可压缩,必须完整保留) +- "尾"是什么? 最终推理步骤(列出公式 → 代入数值 → 计算结果 → 对比选项 → +输出答案)。这一段的推理链必须完整保留,因为任何压缩都可能导致逻辑跳跃 +- "中间"是什么? 文档阅读过程。比如读了一份 50 页的保险条款,中间可能读了 +20 页才找到"现金价值"的定义。这 20 页的阅读过程可以压缩为一段结构化摘要:"该 +产品的现金价值计算规则:第 1-5 年为已交保费 × 100%,第 6-10 年为已交保费 × +75%,第 10 年后为已交保费 × 50%。退保费用率为逐年递减,第 7 年起为 0%。" +- 摘要替代:用 LLM(或同一个小模型)生成文档阅读摘要,替代原始阅读内容。 +摘要的关键要求是:结构化、数值精确、标注出处(页码/段落号)。 +Claude Code 的四层渐进压缩 [21] +Claude Code 把压缩拆成四层,从便宜到贵逐级尝试: +Plaintext +Layer 1: Snip(裁剪工具输出)→ 免费,不调 LLM +Layer 2: Microcompact(基于 prompt cache 的轻量压缩) +Layer 3: Context Collapse(折叠搜索/读取操作为摘要) +Layer 4: Autocompact(LLM 全量总结)→ 最贵 +核心设计原则:只有低层搞不定才升级。Snip 只是把老工具结果截断为头尾各保留一 +部分,不消耗 token;Autocompact 要调一次 LLM 生成全量摘要。如果 80% 的场景 +Snip 就够了,只有 20% 需要 LLM 介入,整体 token 效率远高于每次都调 LLM。 +对本赛题的启发:检索返回的文档片段如果太长,先 Snip(只保留包含关键词的段落 ++ 前后各一段上下文);Snip 后仍然超预算再调 LLM 做摘要。不要一上来就让模型总 +结。 +Codex 的 Memento 策略:为什么不能只留摘要 [22] +Codex 的 compaction 有一个关键设计:压缩后的历史不是纯摘要,而是 +initial_context + 精选 user 消息 + summary + 最近 N 条。原因很简单, +LLM 生成的 summary 会丢失精确信息。 +一个本赛题中的典型场景:保险条款里"因意外伤害发生上述情形的,无等待期"这句例 +外条件,压缩时可能被总结为"等待期内一般不赔","意外伤害例外"这个关键信息就丢 + +--- + +## 第 29 页 + +了。如果后续推理依赖这个例外条件判断选项对错,摘要导致的错误不可逆。 +Codex 的做法是:user 原始消息尽量保留(最多 20k tokens),只在实在放不下时才 +把最早的 user 消息纳入摘要。摘要负责"大局观"(任务进展、关键决策),原始消息负 +责"精确性"(具体数值、条件判断、例外条款)。 +对本赛题来说,这意味着压缩时至少要保留三类信息不被摘要化: +## 1. 题目原文(包括选项):压缩题目等于修改题目 +## 2. 从文档中提取的精确数值和公式:如"现金价值 = 累计保费 × 75%" +## 3. 包含例外条件的条款原文:如"但意外伤害导致的情形除外" +generic-agent 的 L1-L4 记忆层级 [24] +这是最系统的分层方案,来自一个面向长期自主运行的 Agent 项目。虽然不是专门为 +本赛题设计的,但其分层哲学和信息分类决策树可以直接借用。 +四层架构的完整定义: +L1:全局内存索引(global_mem_insight.txt) +硬约束 ≤30 行、期望 <1k tokens。内容只包含: +- 场景关键词 → 记忆定位映射:高频场景直接给出 SOP 文件名或 L2 节名;低频 +场景仅列关键词,需要时再 read L2 或 ls L3 +- RULES(避坑准则):压缩版的红线规则和高频犯错点。每条不超过一句话,如" +搜索用 google 不用百度""禁无条件杀 python(会杀自己)" +L1 的更新纪律极严:L2/L3 有新增/删除时才更新 L1;修改时不允许 overwrite 或 +code run,只能少量 patch。不写 How-to、不写技术细节、不写日志。 +L2:全局事实库(global_mem.txt) +存储环境特异性事实:IP、非标路径、凭证、配置常量等。按 ## [SECTION] 组织。 +这些是 LLM 零样本无法推理出来的信息。禁止存储易变状态、猜测、或通用常识。 +L3:任务级精简记录库(../memory/) +为特定任务保留的极简 SOP 和工具脚本。只记录跨会话仍重要、且难以通过少量探测 +快速重建的要点: +- 该任务特有的隐藏前置条件 +- 典型易踩坑点(一旦遗忘会导致高成本重试的信息) +- 高复用的处理脚本(封装复杂逻辑,避免每次重新推理) +不记录普通操作步骤或可在几步探测中重新获得的路径信息。 + +--- + +## 第 30 页 + +L4:历史会话层(L4_raw_sessions/) +由调度器自动收集的原始对话记录,用于回溯过往上下文。这一层不主动维护,只在 +需要时被检索。 +信息分类决策树: +Plaintext +"这条信息该放哪层?" +是环境特异性事实(IP、凭证、ID 等)? +├─ YES → L2,然后按频率归入 L1 +└─ NO → 是通用操作规律(全局避坑准则)? +├─ YES → L1 RULES(仅限 1 句压缩准则) +└─ NO → 是特定任务技术(艰难尝试才成功,未来还能用)? +├─ YES → L3(专项 SOP 或脚本) +└─ NO → 丢弃(判定为通用常识或冗余信息) +核心公理: +## 1. 行动验证原则:任何写入 L1/L2/L3 的信息,必须源自成功的工具调用结果。严禁 +将模型的固有知识、推理猜测、未执行的计划作为事实写入。口号:No Execution, +No Memory。 +## 2. 神圣不可删改性:凡是经过行动验证的有效配置、避坑指南、关键路径,在重构 +时严禁丢弃。可以压缩文字、可以迁移层级(从 L2 移到 L3), 但不能丢失信息的准确 +性和可追溯性。 +## 3. 禁止存储易变状态:当前时间戳、临时 Session ID、运行中的 PID、某个具体绝 +对路径。这些随时间/会话高频变化的数据严禁写入记忆。 +## 4. 最小充分指针:上层只留能定位下层的最短标识,多一词即冗余。 +对本赛题的启发: +L1-L4 的分层哲学可以直接映射到单题推理的信息管理: +- L1 等价物:题目的结构化索引,"这道题属于公式计算题,涉及文档 A 的现金价 +值公式和文档 B 的退保费用表"。这是一句话的摘要,帮助模型快速定位需要精读的文 +档段落。 +- L2 等价物:从文档中提取的"硬事实":公式、数值表、条款编号、条件判断。这 +些是 LLM 无法凭空生成的,必须从文档中精确提取。 +- L3 等价物:针对特定题型的推理 SOP,比如"计算题 SOP:1) 定位公式 → 2) 提 +取参数 → 3) 代入数值 → 4) 计算结果 → 5) 匹配选项"。每类题型对应一套 SOP。 +- L4 等价物:完整的文档原文(不压缩,保留在外部存储中,只在需要精确引用时 + +--- + +## 第 31 页 + +调取)。 +"行动验证原则"在本赛题中的对应是:只有从文档原文中精确匹配到的信息才能作为答 +案依据。模型的推理猜测("看起来像是 75%")不能作为证据,必须有原文引用。 +"最小充分指针"的对应:在对比多份文档时,带着每份文档的结构化摘要("产品 X 的 +身故保险金 = max(已交保费, 现金价值)"), 摘要中标注原文出处/段(落页号)码,需 +要验证时再回溯原文,不需要带着完整原文。 +学术界的方案:MemGPT 与记忆管理研究 +MemGPT(UC Berkeley, 2023)[16] 提出了一个根本性的视角转换:给 LLM 一个分 +层的记忆系统,让它自己管理"什么在窗口内、什么在窗口外",而不是试图扩展上下文 +窗口本身。 +MemGPT 的核心类比是操作系统的虚拟内存管理: +- 主上下文(Main Context):LLM 的固定上下文窗口,类比物理 RAM。容量有限 +(如 4K/8K/32K tokens), 但访问速度最L快LM。在这里做推理。 +- 外部上下文(External Context):几乎无限的长期存储,类比磁盘。存储完整的 +对话历史、文档、用户信息等。访问速度慢(需要检索),但容量几乎不受限制。 +MemGPT 的关键创新在于:LLM 自己控制"换页"过程。 传统做法是开发者写死截断 +策略(取最后 N 轮对话),MemGPT 的做法是让 LLM 通过 Function Calling 主动管理 +记忆: +- 当主上下文快满时,LLM 可以调用 conversation_search 或 +archival_memory_search 来检索需要的信息 +- LLM 自己判断当前上下文中哪些信息不再需要,将其"换出"到外部存储 +- LLM 自己判断下一步推理需要哪些外部信息,将其"换入"主上下文 +这本质上是把记忆管理从"系统层的固定规则"升级为"应用层的智能决策"。 +MemGPT 的记忆类型: +## 1. 会话记忆(Conversational Memory):对话历史的滚动窗口。超出窗口的历史被 +自动分页存储,LLM 通过 conversation_search 函数按日期或关键词检索。 +## 2. 档案记忆(Archival Memory):文档、文章、知识库等外部资料。LLM 通过 +archival_memory_search 函数做语义检索。这部分最接近本赛题的"文档检索"场景。 +## 3. 工作记忆(Working Memory):当前推理状态。LLM 可以通过 +core_memory_append 和 core_memory_replace 函数主动更新(比如"用户刚才说了 +喜欢 Python,我把这个记下来")。 + +--- + +## 第 32 页 + +后续发展: +- MemoryOS [25] 在 MemGPT 基础上扩展为完整的记忆操作系统,引入记忆的热 +度机制(频繁访问的信息自动提升到更快的存储层)、用户画像的持续更新、以及记忆 +的主动遗忘策略(基于艾宾浩斯遗忘曲线)。 +- A-Mem(Agentic Memory) [26] 进一步引入了结构化笔记和知识网络:LLM 动 +态生成结构化的笔记,并在笔记之间建立链接,形成一个可演化的知识网络,而不是 +简单地把信息存为文本块。 +对本赛题的迁移: +MemGPT 的主上下文 / 外部上下文双层模型可以直接映射到本赛题的推理架构: +关键差异:本赛题是"长文档 + 短推理",MemGPT 是"短文档 + 长对话"。所以 +MemGPT 的"换页"逻辑在本赛题中应该调整为"分段读取 + 渐进压缩":把读过的文档 +段落压缩后换出,释放上下文空间给下一个段落。 +具体来说,读一份 50 页的保险条款时: +## 1. 读第 1-5 页 → 提取关键信息(产品名称、保险期间、基本保额)→ 压缩为结构化 +摘要,存入"外部上下文"的等价物(Python 变量或临时文件) +## 2. 读第 6-10 页 → 提取退保规则 → 追加到摘要 +## 3. 读第 11-15 页 → 发现是模板化条款(释义、投保须知)→ 跳过,不消耗主上下 +文 +## 4. ...继续,直到模型判断信息足够回答问题 +## 5. 最终提交给 LLM 的是结构化摘要而非 50 页原文 + +### 表格 1 + +| MemGPT 概念 | 本赛题等价物 | +| --- | --- | +| 主上下文 | 当前题目的 prompt(题目 + 检索结果 + 推理过程),受
单题 Token 预算约束 | +| 外部上下文 | 文档全文(已解析为 Markdown 的完整内容),不受单
题 Token 预算约束 | +| 换页决策 | 模型判断当前检索结果是否足够回答问题,不够则从文
档中检索更多内容 | +| 会话记忆 | 前一道题的推理结果(如果有跨题关联) | +| 档案记忆 | 文档的 BM25/向量索引 | + +--- + +## 第 33 页 + +对本赛题的迁移 +上述所有方案都针对"长对话"场景,但本赛题的场景是"长文档 + 短推理":每道题是一 +轮独立的推理,不需要跨题记忆。动态压缩在本赛题中应该用在单题推理链的内部: +## 1. 文档阅读阶段:分段读取,每读一段就压缩提取关键信息(公式、数值、条件判 +断),只保留压缩后的摘要进入下一段 +## 2. 多文档对比阶段:读完文档 A 后生成结构化摘要(如"产品 X:身故保险金 = +max(已交保费, 现金价值)"),读文档 B 时只带着 A 的摘要而非原文,对比时用摘要即 +可 +## 3. 验证阶段:自检时不需要重新读全文,只需要带上第一轮推理的结构化摘要(公 +式 + 代入值 + 计算结果)做交叉验证 +具体实现可以参考 Hermes 的 trajectory compressor 思路:保护题目的完整文本 +(head),对中间文档阅读过程做压m缩idd(le),保留最终推理步骤的完ta整il)链。( +### 4.5 实现建议:从策略到代码 +上面讨论的方案要落地到本赛题,需要回答三个具体问题:什么时候触发压缩、哪些 +内容保留、摘要用什么格式。 +触发策略 +参考 Kimi CLI 的双条件触发(tokens >= 上限 × 比例或 tokens + 预留 >= 上限), +对本赛题的建议: +Plaintext +需要压缩 = (当前 prompt tokens >= 模型上下文窗口 × 0.7) +或 (当前 tokens + 预计下一轮检索返回的 tokens >= 模型上下文 +窗口) +### 0.7 这个阈值是 Claude Code [21] 和 CheetahClaws [23] 的共同选择:给后续推理留 +30% 余量。对于本赛题的单题推理场景,Qwen-plus 从 Qwen-Plus-2025-07-28 快照 +版本起支持 1M 上下文(默认 128K,可通过 max_input_token 参数调整)[27],即 +使按默认 128K 计算,约在 90K tokens 时触发第一次压缩。考虑到单篇年报解析后约 +5-8 万字符(折合 4-6 万 tokens),读两篇年报就可能触发。 +保留策略(Memento 模式) +按优先级从高到低保留: +## 1. System prompt + 题目原文:不压缩,始终保留 +## 2. 从文档中提取的精确数值和公式:保留原文,不压缩 +## 3. 包含例外条件的条款原文(如"但...除外""不适用...情形"):保留原文 + +--- + +## 第 34 页 + +## 4. 中间推理步骤:可压缩为结构化摘要 +## 5. 已读但未用到的文档段落:直接丢弃 +具体实现:每次读完一段文档后,先让模型做一个"提取"操作:把这一段里的公式、数 +值、例外条件摘出来,只保留提取结果进入下一段。这样读 10 段文档可能只需要保留 +500 tokens 的提取结果,而不是 5000 tokens 的原文。 +结构化摘要模板 +压缩时不要用 plain text 总结,用结构化模板。参考 Hermes 的格式: +Plaintext +[CONTEXT SUMMARY] +Goal: 判断四款产品的身故保险金高低 +Documents Read: doc_1(平安智盈金生)、doc_2(国寿增益宝) +Key Formulas Extracted: +- 平安智盈金生: 身故保险金 = max(已交保费, 现金价值) +- 国寿增益宝: 身故保险金 = 基本保额 × 年龄系数 +Numerical Values: +- 平安: 已交保费=100 万, 现金价值=90 万 → max = 100 万 +- 国寿: 基本保额=90 万, 系数=1.6 → 144 万 +Pending Verification: 文档 3 和文档 4 尚未读取 +这种格式的好处是:后续推理可以直接定位到需要的字段,不需要在摘要里重新搜索。 +而且结构化的 Key Formulas 和 Numerical Values 不会被 LLM 的总结语言"改写"导致 +精度丢失。 + +--- + +## 第 35 页 + +## 5. 答案验证与多轮推理 +### 5.1 自检机制 +Baseline 单轮提问、单轮回答,没有验证环节。可以加入自检: +## 1. 第一轮:让模型根据上下文生成答案 +## 2. 第二轮:让模型验证自己的答案,检查每个选项的证据是否充分 +## 3. 如果不一致,回退到第一轮重新生成 +### 5.2 常见错误模式 +从实验数据中总结的典型错误: +- 否定词遗漏:文档说"不适用",模型读成了"适用" +- 年份混淆:题目问 2025 年数据,模型用了 2024 年的 +- 条件遗漏:只读到"免赔额 5000 元",漏掉了"意外伤害 0 免赔" +- 跨文档混淆:把文档 A 的公式套到了文档 B 的场景上 +可以在 prompt 中针对这些错误模式加入显式检查指令。 +## 6. Prompt 工程 +### 6.1 题型差异化 Prompt +不同题型需要不同的引导策略: +- 计算题:要求模型先列出公式、再代入数值、再计算结果 +- 条款定位题:要求模型先定位条款原文、再逐条比对 +- 多选题:要求模型逐选项判断,最后汇总 +### 6.2 Few-shot 示例 +在 prompt 中提供 1-2 个已答对的题目作为示例,帮助模型理解答题格式和推理过程。 +注意示例不要占用太多 token。 + +--- + +## 第 36 页 + +## 7. Agent 化方向 +### 7.1 Function Calling 方案 +Baseline 测试过 Function Calling,Token 消耗比 no-tool 高约 18%。但 FC 的优势在 +于模型可以自主决策"什么时候搜、搜什么"。 +适合 FC 的场景:跨文档题(需要逐份文档搜索)、信息不足时需要补搜的场景。 +### 7.2 多步推理 +从"读文档 → 答问题"升级为"读文档 → 提取关键信息 → 判断信息是否足够 → 不够则 +继续搜索 → 综合推理 → 输出答案"的多步流程。 +### 7.3 终端 Agent 与 Bash 工具 +前面讨论的 Agent 大多停留在"读文档 + 调 LLM"的层面。但在本赛题中,有一类题目 +单纯靠 LLM 的文本推理能力是处理不好的:公式计算题。 +LLM 的计算短板 +大语言模型本质上是"文字接龙"引擎,不是计算器。它在做数学运算时其实是在"背诵" +训练数据里见过的计算结果,而不是真正执行算术。举例: +- 题目要求计算 (8 亿 × 6.67% + 12 亿 × 75%) / (1 - 30%) +- LLM 直接推理:可能在某一步算错中间结果,或者搞混运算顺序 +- 结果:选项 A 和选项 B 的数值差 0.3%,LLM 选错了 +这不是模型不够聪明,而是概率生成天然不适合精确计算。研究(ARTIST, 2025)表 +明,即使是最强的 LLM 在多步数值计算中的错误率也显著高于使用 Python 解释器辅 +助的方案。 + +--- + +## 第 37 页 + +解决方案:让 LLM 写代码而不是算答案 +核心思路:不让 LLM 自己算,而是让它写一段 Python 代码,用 Bash 工具执行代码, +再把执行结果还给 LLM 做最终判断。 +这是终端 Agent(Terminal Agent / Coding Agent)模式在金融 QA 中的典型应用。参 +考 Claude Code(Anthropic, 2025)和 SWE-bench 中的 Agent 架构,它们共同的特 +点是: +## 1. 模型不直接输出答案,而是输出可执行的代码 +## 2. 通过 Bash 工具执行代码,获取精确的数值结果 +## 3. 模型拿到结果后再做语义层面的判断 +本赛题中的具体应用 +公式计算题(如 ins_a_001 四产品身故保险金排序、ins_a_002 退保金排序): +模型推理: "国寿增益宝的身故保险金 = 基本保额 × 系数, +系数取决于被保险人年龄。40 岁对应系数为 1.6。" +模型生成代码: +```python +# 国寿增益宝 +``` +basic_amount = 900000 # 基本保额 90 万 +coefficient = 1.6 # 40 岁对应系数 +guoshou = basic_amount * coefficient +# 平安智盈金生 +total_premium = 1000000 # 已交保费 100 万 +account_value = 900000 # 保单账户价值 90 万 +pingan_zhiying = max(total_premium, account_value) +# 排序 +results = sorted([ +("国寿增益宝", guoshou), +("平安智盈金生", pingan_zhiying), +], key=lambda x: x[1], reverse=True) +print(results) +Bash 执行代码 → 输出精确结果 → 模型判断哪个选项匹配。 +跨文档数值对比题(如 fin_a_001 比亚迪两年年报对比): +模型提取数据: "2024 年营收 6023 亿,2025 年营收 7231 亿" + +--- + +## 第 38 页 + +模型生成代码: +```python +revenue_2024 = 6023 +revenue_2025 = 7231 +growth = (revenue_2025 - revenue_2024) / revenue_2024 * 100 +``` +print(f"增长率: {growth:.1f}%") +Bash 执行 → 增长率: 20.1% → 模型判断"是否实现增长"。 +为什么 Bash 工具比 Function Calling 更适合计算 +Function Calling 模式通常是这样:模型调用 +calculator(expression="8*0.0667"),然后拿到返回值。这可以处理简单运算, +但有两个局限: +## 1. 不能写多步逻辑:复杂公式需要中间变量、条件判断、循环,一个 calculator() +函数不够用 +## 2. 不能做数据整理:跨文档对比需要先把多个文档的数据提取出来,再统一计算。 +Bash 执行的 Python 脚本可以自由定义数据结构 +Bash 工具的本质是给模型一个完整的编程环境,而不是几个预定义的函数。这让模型 +可以自己决定"先算哪个、后算哪个、中间结果怎么存、最终怎么排序"。 + +--- + +## 第 39 页 + +参考架构:ReAct 循环 + Bash 工具 +ReAct(Reasoning + Acting, Yao et al., 2023)是目前最主流的 Agent 推理范式,也 +是 Claude Code、OpenHands 等终端 Agent 的基础。 +ReAct 的循环是:思考(Thought)→ 行动(Action)→ 观察(Observation)→ +思考 → ... +在本赛题中: +Plaintext +Thought: "这道题需要计算三份保险的退保金。 +先从文档中提取各产品的退保金公式和参数。" +Action: 搜索文档,提取公式 +Observation: "国寿增益宝:退保金 = 个人账户价值 × (1-退保费用率) +退保费用率第 7 年为 0%" +Thought: "公式和参数已提取,现在用 Python 计算。" +Action: Bash 执行 Python 代码 +Observation: "国寿增益宝 = 120000, 平安智盈金生 = 115000, +平安富鸿金生 = 90000" +Thought: "排序:120000 > 115000 > 90000, +对应选项 A:国寿 > 智盈 > 富鸿。" +这个循环的关键在于:Thought 和 Action 交替进行,模型每一步都基于上一步的观 +察结果做决策,而不是一次性把所有动作都规划好再执行。 +实现建议 +- 在 Function Calling 的 tools 定义中加入 execute_python(code: str) 工具 +- 工具描述中明确说明:用于精确数值计算,不要用 LLM 自己做算术 +- 模型输出代码后,在沙箱中执行(用 subprocess 或 exec), 返回stdout/stderr +- 代码执行超时设为 5 秒,防止死循环 +- 如果代码报错,把错误信息返回给模型,让模型自己修 +一个额外的收益:Bash 执行的 Token 消耗远低于让模型一步步推理。一段 Python 代 +码 50 个 token,执行结果 10 个 token;而同样的计算让模型用 CoT 推理可能需要 +200+ token。 + +--- + +## 第 40 页 + +### 7.4 smolagents:用 Code Agent 范式重新组织工具调用 +什么是 smolagents +smolagents(HuggingFace, 2025 年发布,持续更新中, GitHub: +huggingface/smolagents)[13] 是一个轻量级的 Agent 框架,GitHub 超过 24,000 星 +标。它的核心设计理念是:让 Agent 通过编写 Python 代码来执行操作,而不是通过 +JSON 格式的工具调用。 +这个区别看似只是"输出格式不同",实际上影响了 Agent 的整个推理效率。 +传统 Tool Calling vs Code Agent +传统 Function Calling 的流程: +Plaintext +模型推理 → 输出 JSON {"tool": "search", "params": {"query": "退保金 +"}} +→ 外部系统解析 JSON → 执行工具 → 返回结果 +→ 模型再推理 → 输出 JSON {"tool": "calculator", ...} +→ 外部系统再解析 → ... +每一步都需要 JSON 序列化/反序列化,且模型只能一次调用一个工具(或并行的几个 +工具),无法写条件判断、循环、中间变量等编程逻辑。 +Code Agent 的流程: +模型推理后直接写一段 Python 代码: +```python +# 第一步:搜索退保条款 +``` +results = search("退保金计算公式") +# 第二步:提取数值并计算 +premium = 100000 # 从搜索结果中提取 +interest = 20000 +cash_value = premium + interest * 0.75 +# 第三步:输出结果 +final_answer(f"退保金为 {cash_value} 元") +代码一次性执行完毕,中间结果自动传递,不需要反复序列化。 + +--- + +## 第 41 页 + +效率提升 +据 HuggingFace 的实验数据,Code Agent 相比 JSON Tool Calling 减少了约 30% 的 +推理步骤。原因是: +## 1. 批量操作:一次代码块可以包含多个工具调用,不需要"调一个 → 等结果 → 再调 +下一个" +## 2. 变量传递:中间结果存为 Python 变量,自动流入下一步,不需要 JSON 来回传 +## 3. 条件逻辑:可以用 if/else 做分支判断,比如"如果搜索结果不相关,换个关键词 +重搜" +## 4. 模型原生能力:LLM 在代码生成上的训练数据远多于 JSON 工具调用格式,生成 +质量更稳定 +对本赛题的适用性 +smolagents 天然适合本赛题的几个场景: +公式计算题:直接写 Python 代码做数值计算,精确无误。 +跨文档对比题:一次代码块中调用多次 search 工具(分别搜不同文档),收集所有数 +据后再统一计算。 +多步推理题:用 if/else 做条件分支。比如"如果资产负债率超过 70%,触发特别决 +议;否则普通决议"。 +自检题:代码中可以加入断言或验证逻辑。比如 assert result > 0, "计算结果不 +应为负"。 + +--- + +## 第 42 页 + +进阶 baseline 集成方案 +在 advance-baseline/ 下新建一个基于 smolagents 的进阶版本,架构如下: +Plaintext +advance-baseline/ +├── src/ +│ ├── __init__.py +│ ├── agent/ +│ │ ├── __init__.py +│ │ ├── smol_agent.py # smolagents CodeAgent 主逻辑 +│ │ └── tools.py # 自定义工具:文档搜索、BM25 检索、 +Python 执行 +│ ├── data/ +│ │ ├── __init__.py +│ │ └── loader.py # 题目加载 + 文档读取(复用 baseline +逻辑) +│ ├── evaluation/ +│ │ ├── __init__.py +│ │ └── evaluator.py # 答案校验与格式化 +│ └── utils/ +│ ├── __init__.py +│ ├── config.py +│ └── llm_client.py # LLM 客户端(复用 baseline 逻辑) +├── config.yaml +├── requirements.txt +└── run_agent.py # 入口 +关键设计决策: +## 1. 自定义工具集:注册 search_document(query, doc_id) 和 +execute_python(code) 两个核心工具,smolagents 的 @tool 装饰器让工具定义极 +其简洁 +## 2. 模型无关:smolagents 支持百炼(OpenAI 兼容 API)和阶跃星辰,通过 LiteLLM +适配,环境变量切换即可 +## 3. 安全执行:Python 代码在受限的 LocalPythonInterpreter 中执行,限制 +import os/subprocess 等危险模块 +## 4. Token 预算控制:smolagents 本身不额外消耗 Token,但 Code Agent 的输出比 +JSON Tool Calling 更紧凑 + +--- + +## 第 43 页 + +与 baseline 的对比 + +### 表格 1 + +| 维度 | baseline (no-tool) | smolagents 进阶版 | +| --- | --- | --- | +| 文档处理 | 硬截断前 4000 字符 | BM25 检索 + Code Agent 自主决
定搜索策略 | +| 计算能力 | LLM 直接推理(易出错) | Python 代码执行(精确) | +| 推理模式 | 单轮 | ReAct 多轮循环 | +| 跨文档 | 所有文档拼接截断 | Code Agent 逐份搜索、聚合计算 | +| Token 效率 | 固定消耗 | 按需搜索,代码执行不消耗 LLM
Token | +| 实现复杂度 | 低 | 中(需要理解 smolagents 框架) | + +--- + +## 第 44 页 + +附录 A:技术概念速查 +本附录面向第一次接触 Agent、RAG、Token 计算等技术概念的参赛者。如果教程正 +文中提到的某个术语让你感到陌生,先来这里查找。 +A.1 什么是 Agent(智能体) +Agent 是指大语言模型不再只做"你问我答"的被动响应,而是能够主动观察环境、做 +出决策、调用工具、并根据反馈调整下一步行动的系统。 +用一个比喻说明区别: +- 单次调用:你问"今天北京天气怎么样",模型直接回答。这是"一问一答",没有后 +续动作。 +- 工作流(Workflow):你让模型"先查天气,再推荐穿衣",但每一步都是你提前写 +死的,模型只是按顺序执行。比如第一步固定调用天气 API,第二步固定调用穿衣推 +荐模板。 +- Agent:你给模型一个目标"帮我规划一次北京三日游",模型自己决定:第一步查 +天气,第二步搜景点,第三步查交通,第四步汇总成行程表。如果查到的景点因天气 +原因不适合,模型会自己回到第一步重新查,而不是死板地继续执行原计划。 +本赛题叫"金融长文本 Agent 的动态记忆压缩与高效问答挑战",这里的 Agent 强调的 +是模型在读取长文档的过程中,能够动态决定保留哪些信息、压缩哪些信息、什么时 +候停止阅读。本 baseline 的硬截断方案其实不算真正的 Agent,因为它没有任何决策 +能力,只是固定截断 4000 字符。 +为什么赛题强调 Agent 而不是简单的问答 +因为如果只是"把文档塞给模型、让模型选答案",那和普通的 RAG 没有区别。赛题的 +难点在于文档太长(一篇年报 10 万字),不能全部塞进去,所以系统必须具备自主决 +策能力:判断哪些内容需要精读、哪些可以跳过、读完一遍后信息是否足够、不够的 +话再从哪里补。这种"读一点、判断一下、再决定读不读"的循环,才是 Agent 的核心 +特征。 +A.2 Embedding 与向量检索 +Embedding(嵌入)是一种把文本转换成数字向量的技术。它的核心思想是:意思相 +近的文本,在向量空间中的位置也相近。 +具体过程: +## 1. 选一个 Embedding 模型(如 text-embedding-3、bge-large-zh), 输入一段文本 +## 2. 模型输出一个固定长度的向量(比如 768 维或 1536 维),这串数字就是这段文本 + +--- + +## 第 45 页 + +的"指纹" +## 3. 两段文本如果语义相近(比如"退保费用"和"退保手续费"), 它们的向量指纹也会 +很接近 +向量检索就是把所有文档片段的向量存进数据库(如 Milvus、Qdrant、FAISS),收到 +问题后,把问题也转成向量,计算它和所有片段向量的相似度,取最相似的前 K 个。 +计算相似度最常用的方法是余弦相似度:把两个向量看成空间中的两个箭头,夹角越 +小(方向越一致),相似度越高。余弦相似度的取值范围是 -1 到 1,实际使用时通常 +只关心 0 到 1 的区间,0.8 以上就算比较相似。 +Embedding 检索 vs 关键词搜索 +本赛题没有采用 Embedding 检索,原因见 3.2 节混合检索的讨论:官方未明确 +Embedding 模型是否可用,以及其 Token 消耗如何计入总预算。 + +### 表格 1 + +| 维度 | 关键词搜索(如 BM25) | Embedding 向量检索 | +| --- | --- | --- | +| 匹配依据 | 词是否相同 | 语义是否相近 | +| "退保费用"搜"退保手续费" | 搜不到(词不同) | 能搜到(语义相近) | +| "2025 年营收"搜"去年收
入" | 搜不到 | 可能搜到 | +| 对数字/年份的敏感度 | 高 | 低 | +| 计算开销 | 低 | 高(需要 GPU/专用模
型) | +| 是否需要外部模型 | 否 | 是 | + +--- + +## 第 46 页 + +A.3 Rerank(重排序) +Rerank 是在初步检索结果之上做二次精排的技术。 +为什么需要 Rerank?因为初步检索(无论是 BM25 还是 Embedding)追求速度,通 +常用比较轻量的算法,结果中可能混入一些"看起来相关但不精确"的片段。Rerank 用 +更精细的模型(通常是 Cross-Encoder)对 top K 结果逐一打分,把真正相关的排在 +前面。 +具体流程: +## 1. 粗排(召回):用 BM25 或 Embedding 快速从上万片段中挑出 50-100 个候选 +## 2. 精排(Rerank):把"问题 + 每个候选片段"一起送入 Cross-Encoder,模型输出一 +个相关性分数 +## 3. 取 top K:按分数排序,只把最高分的 5-10 个片段交给 LLM +Cross-Encoder 的工作原理和 Embedding 不同:Embedding 是分别把问题和片段转 +成向量再比较;Cross-Encoder 是把问题和片段拼接成一段文字一起输入模型,让模 +型直接判断"这段文字和问题有多相关"。Cross-Encoder 更精确,但速度更慢,所以只 +用在少量候选上。 +搜索引擎(百度、谷歌、夸克)的搜索结果页其实也是这个逻辑:先通过倒排索引快 +速召回百万网页,再用复杂的排序算法(考虑了上百个特征)把最相关的排到前面。 +A.4 BM25 +BM25 是一种基于词频统计的文本检索算法,是信息检索领域最经典、最常用的算法 +之一。它的核心思想很简单:如果一个词在文档中出现次数多、在整体文档集中出现 +次数少,那么这个词对该文档的区分度就高。 +BM25 计算每个词对文档的相关性贡献: +- 词频(TF):词在文档中出现的次数。出现越多,贡献越大。但不是线性增长(出 +现 100 次不代表比出现 10 次重要 10 倍),BM25 用一个对数函数做饱和处理。 +- 逆文档频率(IDF):词在所有文档中出现的频繁程度。"的""是"这种词在每篇文档 +都出现,IDF 很低;"退保费用"只在保险条款中出现,IDF 很高。 +最终得分 = 所有查询词的 TF × IDF 之和。 +BM25 的优点: +- 纯统计方法,不依赖任何外部模型,计算速度快 +- 对精确匹配(如数字、年份、条款编号)很敏感 +- 实现简单,jieba 分词 + rank-bm25 库几行代码就能跑 + +--- + +## 第 47 页 + +BM25 的缺点: +- 无法理解语义。"退保费用"和"退保手续费"会被当成完全不同的词 +- 对同义词、近义词无能为力 +- 长文档天然占优(词频容易更高),需要做长度归一化 +A.5 Token 与上下文窗口 +Token 是大语言模型处理文本的基本单位。它不是字符,也不是单词,而是模型在训 +练时从海量文本中学到的"词片段"。 +中英文的 Token 换算大致如下: +- 1 个英文单词 ≈ 1-2 个 Token +- 1 个汉字 ≈ 1-1.5 个 Token(具体取决于模型 tokenizer) +- 1 个标点符号 ≈ 1 个 Token +- 数字通常逐位拆分为 Token("2025"可能是 2 个 Token:"20"+"25") +举例:"中国平安的 2024 年营业收入是 1 万亿元"这句话,Qwen 系列模型大约需要 +15-20 个 Token。 +上下文窗口(Context Window) 是指模型一次能处理的 Token 数量上限。Qwen- +plus 从 Qwen-Plus-2025-07-28 快照版本起支持 1M Token(约 80 万汉字),默认 +128K(约 10 万汉字),可通过 max_input_token 参数调整 [27]。看起来很长,但实 +际使用时有两个约束: +## 1. 经济约束:prompt 越长,消耗的 Token 越多,费用越高。本赛题 500 万 Token +的总预算,意味着你全部用完的话,平均每条 prompt 只能花 5 万个 Token(100 题)。 +如果把 10 万字的年报全文塞进去,一道题就花掉 10 万 Token,100 题就超预算了。 +## 2. 注意力衰减:即使模型技术上支持 128K 上下文,实验表明模型对 prompt 中间部 +分的内容记忆效果会下降(称为"中间丢失"问题)。关键信息如果藏在 prompt 中段, +模型可能"视而不见"。 +这就是本赛题"动态记忆压缩"的核心挑战:不能无脑塞全文,必须在"保留足够信息"和 +"控制 Token 消耗"之间做取舍。 + +--- + +## 第 48 页 + +附录 B:五类金融文档背景速查 +本附录介绍赛题涉及的五种金融文档类型,帮助没有金融背景的参赛者快速理解这些 +文档的结构和阅读重点。文档类型不同,题目考查的角度也不同,理解文档结构有助 +于设计更有针对性的检索和阅读策略。 +B.1 保险条款 +保险条款是保险公司与被保险人之间的合同文本,具有法律效力。条款的措辞非常精 +确,任何模糊表述都可能导致理赔纠纷。 +典型结构: +## 1. 保险责任:保什么。比如"身故保险金""重大疾病保险金""住院医疗费用" +## 2. 责任免除:不保什么。通常用"因下列情形之一导致...的,本公司不承担保险责任" +开头。这是条款定位题的高频考点。 +## 3. 保险期间与等待期:等待期内出险通常不赔或只退保费,但意外伤害导致的出险 +往往例外("因意外伤害发生上述情形的,无等待期")。 +## 4. 保险金额与保费:基本保额、累计保费、个人账户价值之间的关系。 +## 5. 退保与现金价值:退保能拿回多少钱,通常和已交保费、持有年限、账户收益挂 +钩。不同产品的现金价值计算公式差异很大。 +## 6. 释义:对条款中出现的专业术语(如"意外伤害""重大疾病""医院")给出明确定义。 +答题关键: +- 计算题要精准定位公式和参数位置,注意年限对应的系数 +- 等待期题要找"意外伤害"例外条款 +- 免责范围题要到对应条款的"责任免除"章节逐条核对 +- 跨产品比较题需要同时打开多份条款,提取同类字段 +B.2 监管法规 +监管法规是金融监管机构发布的规范性文件,具有强制约束力。参赛涉及的法规主要 +包括反洗钱、客户尽职调查、受益所有人识别等方面。 +典型结构: +## 1. 总则/适用范围:法规适用的主体、业务类型、地域范围 +## 2. 具体义务:金融机构需要做什么。比如"应当识别受益所有人""应当保存客户身份 +资料至少 5 年" + +--- + +## 第 49 页 + +## 3. 时限要求:很多条款有明确的时间节点("在建立业务关系后 10 个工作日内""在获 +知变更信息后 30 日内") +## 4. 例外条款:"但书"("但是...")后面的内容往往是考点。比如"应当识别受益所有人, +但各级党的机关除外" +## 5. 罚则/法律责任:违反规定会受到什么处罚 +答题关键: +- 法条引用题要找原文出处,不能凭印象判断 +- 时限题要精确定位具体天数/工作日 +- 适用范围题要注意"除外"条款 +- 不同法规之间可能存在冲突或补充关系,需要判断优先适用哪一条 +B.3 财务报表 +财务报表反映企业的经营成果、财务状况和现金流量。赛题涉及的主要是上市公司年 +报中的三大报表。 +三大报表: +## 1. 资产负债表(Balance Sheet) +○ 反映某一时点的财务状况 +○ 基本等式:资产 = 负债 + 所有者权益 +○ 关键科目:货币资金、应收账款、存货、固定资产、短期借款、长期借款、 +股东权益 +## 2. 利润表(Income Statement) +○ 反映一段时间内的经营成果 +○ 基本等式:营业收入 - 营业成本 - 费用 = 净利润 +○ 关键科目:营业总收入、营业成本、销售费用、管理费用、研发费用、营业 +利润、净利润、归属于母公司股东的净利润 +## 3. 现金流量表(Cash Flow Statement) +○ 反映一段时间内的现金流入和流出 +○ 分三类:经营活动、投资活动、筹资活动 +○ 关键指标:经营活动产生的现金流量净额(衡量企业"造血能力"的核心指标) +常用财务指标: + +--- + +## 第 50 页 + +答题关键: +- 跨年度对比题要从两年报表中提取同一指标 +- 跨公司对比题要注意指标口径是否一致 +- 增长率计算要确认基期和报告期 +- 注意"归属于母公司股东的净利润"和"净利润"的区别(前者扣除了少数股东损益) +B.4 金融合同 +赛题中的金融合同主要是债券募集说明书和债券条款。 +典型结构: +## 1. 发行概况:发行人、债券类型、发行规模、期限、利率 +## 2. 募集资金用途:资金投向、使用计划 +## 3. 评级信息:主体评级、债项评级、评级展望 +## 4. 增信措施:担保、抵押、质押等信用增进安排 +## 5. 特殊条款: +○ 回售条款:投资者有权在特定时间按约定价格将债券卖回给发行人 +○ 赎回条款:发行人有权在特定时间提前赎回债券 +○ 调整票面利率条款:发行人有权调整后续计息期间的利率 +○ 交叉违约条款:发行人在其他债务上违约,本债券也视为违约 +答题关键: +- 条款判断题要定位到具体条款原文,不能凭常识 +- 计算题(如回售收益率)要确认时间节点和利率 +- 评级相关题要区分"主体评级"和"债项评级" + +### 表格 1 + +| 指标 | 公式 | 含义 | +| --- | --- | --- | +| 毛利率 | (营业收入 - 营业成本) / 营
业收入 | 产品本身的盈利能力 | +| 净利率 | 净利润 / 营业收入 | 最终盈利能力 | +| ROE(净资产收益率) | 净利润 / 平均股东权益 | 股东投入资本的回报 | +| 研发投入占比 | 研发费用 / 营业收入 | 企业对研发的重视程度 | + +--- + +## 第 51 页 + +B.5 行业研报 +行业研报是券商研究机构发布的对某个行业的深度分析,通常包含数据、观点、预测 +和图表。 +典型结构: +## 1. 行业概况:行业定义、产业链、市场规模 +## 2. 竞争格局:主要参与者、市场份额、集中度(CR4、CR8) +## 3. 驱动因素:政策、技术、需求、供给等影响行业发展的关键因素 +## 4. 数据与预测:历史数据、增长率、未来预测 +## 5. 投资建议:看好/看平/看空,风险提示 +答题关键: +- 事实查找题要注意年份限定("2022 年"和"2023 年"数据可能完全不同) +- 研报中的数据通常有明确来源标注,但模型可能混淆不同来源的数据 +- 研报观点("我们认为...")和数据事实要区分开,观点不一定正确 + +--- + +## 第 52 页 + +参考文献 +[1] Marker: Convert PDF to Markdown Quickly with High Accuracy. VikParuchuri, +2024-2025. GitHub: VikParuchuri/marker +[2] MinerU: An Open-source Solution for Precise Document Content Extraction. +Wang et al., 2024. arXiv:2409.18839. 持续更新至 2026 年 4 月 v3.1.0。GitHub: +opendatalab/MinerU +[3] OmniDocBench: Benchmarking Diverse PDF Document Parsing with +Comprehensive Annotations. Ouyang et al., 2024. arXiv:2412.07626 +[4] olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models. +Poznanski et al. (Allen AI), 2025. arXiv:2502.18443 +[5] From Local to Global: A Graph RAG Approach to Query-Focused Summarization. +Edge et al. (Microsoft Research), 2024. arXiv:2404.16130 +[6] LightRAG: Simple and Fast Retrieval-Augmented Generation. Guo et al. (香港大 +学 HKUDS 实验室), 2024. arXiv:2410.05779. EMNLP 2025 +[7] StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time +Hybrid Information Structurization. Li et al., 2024. arXiv:2410.08815 +[8] RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval. Sarthi +et al., 2024. arXiv:2401.18059 +[9] Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. +Asai et al., 2024. arXiv:2310.11511 +[10] Corrective Retrieval Augmented Generation. Yan et al., 2024. arXiv:2401.15884 +[11] Precise Zero-Shot Dense Retrieval without Relevance Labels (HyDE). Gao et al., +## 2023. arXiv:2212.10496 +[12] Searching for Best Practices in Retrieval-Augmented Generation. Wang et al., +## 2024. arXiv:2407.01219 +[13] smolagents: Minimalist Agent Framework. HuggingFace, 2025. GitHub: +huggingface/smolagents +[14] KAG: Boosting LLMs in Professional Domains via Knowledge Augmented +Generation. Liang et al., 2024. arXiv:2409.13731 +[15] ColPali: Efficient Document Retrieval with Vision Language Models. Faysse et +al., 2024. arXiv:2407.01449. 以及 ColQwen: Visual Document Retrieval with Qwen2- +VL, 2025 +[16] MemGPT: Towards LLMs as Operating Systems. Packer et al. (UC Berkeley), +## 2023. arXiv:2310.08560. 以及 MemoryOS (2025)、A-Mem (2025) 等后续工作 +[17] ReAct: Synergizing Reasoning and Acting in Language Models. Yao et al., 2023. +ICLR 2023. arXiv:2210.03629 +[18] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Lewis et + +--- + +## 第 53 页 + +al., 2020. NeurIPS 2020. arXiv:2005.11401 +[19] Kimi Code CLI. Moonshot AI, 2025. GitHub: moonshotai/kimi-code +[20] Hermes Agent Framework. GitHub: antgroup/hermes +[21] Claude Code. Anthropic, 2025. https://docs.anthropic.com/en/docs/claude-code +[22] Codex. OpenAI, 2025. GitHub: openai/codex +[23] CheetahClaws Agent Framework. GitHub: cheetahclaws/cheetahclaws +[24] generic-agent: Long-Term Autonomous Agent Architecture. GitHub: generic- +agent +[25] MemoryOS: A Memory Operating System for LLM Agents. 2025. +[26] A-Mem: Agentic Memory for LLM Agents. 2025. +[27] 阿里云百炼. Qwen-Plus 模型更新公告(2025 年 8 月 1 日,上下文长度扩展至 +1M). https://www.aliyun.com/notice/detail?notice-id=117430 + +--- diff --git "a/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\345\256\232\347\250\277\347\211\210\343\200\220\350\267\221\351\200\232Baseline\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\345\256\232\347\250\277\347\211\210\343\200\220\350\267\221\351\200\232Baseline\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" new file mode 100644 index 0000000..d7670ef --- /dev/null +++ "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\345\256\232\347\250\277\347\211\210\343\200\220\350\267\221\351\200\232Baseline\343\200\221\350\265\233\351\242\230\345\233\233\357\274\232\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254Agent\347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" @@ -0,0 +1,801 @@ +# 定稿版【跑通Baseline】赛题四：金融长文本Agent的动态记忆压缩与高效问答挑战 + +> 来源：`定稿版【跑通Baseline】赛题四：金融长文本Agent的动态记忆压缩与高效问答挑战.pdf` + +## 第 1 页 + +【跑通 Baseline】赛题四 +金融长文本 Agent 的动态记忆压缩与高效问答挑战 +目标:帮助参赛者快速理解赛题、跑通第一个可提交的基线方案,并明确后续优化方 +向。本 baseline 源码位于项目根目录 src/ 下。题型分析、RAG 概念、金融文档背景 +知识和进阶优化方案请参考进阶教程。 +赛事官方地址:https://tianchi.aliyun.com/competition/entrance/532486 +## 1. 赛题速览 +### 1.1 任务定义 +赛道四的核心任务是金融长文本问答。出题方提供五类金融文档和配套的选择题,要 +求参赛者构建 Agent 系统,在控制 Token 消耗的前提下准确作答。 +赛事官方地址:https://tianchi.aliyun.com/competition/entrance/532486 +大部分题目涉及跨文档对比、公式计算、条款推理和逻辑判断,需要系统具备信息定 +位、知识理解和推理计算三层能力。 +### 1.2 关键约束 +- 模型限制:正式提交只准调用 Qwen 系列模型 API +- 题目范围:A 榜题目附带 doc_ids,系统只需在指定文档中定位答案,不需要全 +网搜索 +- 答案格式:单选(mcq)、多选(multi)、判断(tf)三种 +- 答案标准化规则:单选题和判断题以首个有效答案字母为准;多选题将答案字母 +去重并排序后做完全匹配,不设置部分分,漏选、错选、多选均计为错误 +- Token 预算:500 万 Token 总量限制,评分兼顾准确率与 Token 效率 +### 1.3 评分机制:准确率优先 +最终得分公式大致为准确率 × (0.7 + 0.3 × Token 效率分)。准确率权重占 70%, +Token 效率占 30%。这意味着:即使你把 Token 压得极低,准确率上不去,总分也高 +不了;反之,准确率够高,Token 稍微超一点,对总分的影响有限。因此优化的第一 +优先级是准确率,第二优先级才是 Token 效率。 + +--- + +## 第 2 页 + +### 1.4 数据分布 +A 榜共 100 道题,5 个领域各 20 题,配套 86 份 PDF 文档: +题型分布上,多选题(multi)是重灾区,漏选或多选都不得分,对系统的精确度要求 +最高。单选题(mcq)和判断题(tf)相对简单,但跨文档的 mcq 题同样需要仔细阅 +读多份材料。 +### 1.5 赛题特点解析 +做这道题时,我们发现几个容易被低估的难点: +第一,doc_ids 只是范围提示,不是答案指针。 题目告诉你看哪几份文档,但答案不 +会直接写在文档第一段。很多时候需要把 4 份条款的公式串起来计算,或者对比两份 +年报的同一指标才能判断对错。 +第二,金融知识门槛会过滤掉模型的常识推理。 保险条款中的"等待期""免赔额""责任 +免除",监管法规中的"受益所有人识别""客户尽职调查",这些概念有严格的法律定义 +和适用条件。模型如果没有精准定位到对应条款,凭"常识"推断很容易选错。 +第三,题型之间差异大,没有统一解法。 计算题需要提取公式后代入数值;跨文档对 +比题需要同时打开多份文档提取同一类数据;条款定位题需要精准找到"责任免除""等 +待期"等特定章节;事实查找题需要在几万字的研报中定位一个带年份限定的数字。用 +同一套策略处理所有题型,效果必然打折。 +### 1.6 思维链(CoT)的实验发现 +我们在实验中也测试过让模型"先推理再回答"的 CoT 策略:在 prompt 中要求模型对 +每个选项引用文档条款、说明判断依据,最后再给出答案。 + +### 表格 1 + +| 领域 | 题目数 | 配套文档数 | 文档特点 | +| --- | --- | --- | --- | +| 保险条款 | 20 | 16 | 条款密集,公式多,计算题集中 | +| 监管法规 | 20 | 26 | 法条引用多,跨文档对比题多 | +| 金融合同 | 20 | 14 | 募集说明书、债券条款,数据表格
多 | +| 财务报表 | 20 | 10 | 年报数据量大,财务指标对比题多 | +| 行业研报 | 20 | 20 | 行业分析为主,事实查找题多 | + +--- + +## 第 3 页 + +效果是两极分化: +- 对复杂题有提升。比如跨文档对比题和多选题,模型被迫逐条验证,漏选和误选 +明显减少。部分计算题在 CoT 引导下,提取公式的准确率也有改善。 +- Token 消耗暴涨。baseline 的平均 completion token 只有 2-5 个(模型直接输出 +字母),开启 CoT 后飙升到几百个。100 题跑下来,completion token 可能从 15 万涨 +到 100 万以上,对 500 万总预算的消耗非常可观。 +结论:CoT 可以按需使用,不建议全量开启。在预算有限的前提下,更合理的策略是" +按需开启",只在计算题、多选题、跨文档对比题等复杂题型上启用 CoT,简单的事实 +查找题直接输出答案。这也是后续优化的一个方向。但是我建议都不要开,非必要的 +话。 +### 1.7 关于框架选择 +一个常见的参赛误区是:上来就用 LangChain、LlamaIndex 等现成框架搭建 Agent。 +这些框架为了通用性,在 prompt 模板、工具调用协议、中间状态记录上做了大量封装, +带来的直接后果是 Token 消耗会稍高于自定义设计的流程,如果是大家都能获得答题 +准确率满分的背景下,后面大家应该是需要组自己设计如何组装 prompt 会更节省 +token 的。 +从评分公式看,准确率占 70%、Token 效率占 30%,两者都不是可以忽略的小头: +- 如果参赛者的准确率都能做到不错(比如 60% 以上),这时排名差距主要来自 +Token 效率。谁能在同等准确率下消耗更少的 Token,谁就能拿到更高的效率分。 +- 如果整体准确率都不高(比如普遍低于 40%),这时排名差距主要来自准确率。 +Token 压得再低,准确率上不去也没有意义。 +真正榜单前列的选手,一定在准确率和 Token 消耗上做到了平衡。 框架可以帮你快速 +搭建原型,但在正式提交前,建议把核心流程拆开重写,去掉不必要的 prompt 冗余和 +中间层封装,把 Token 预算用在最关键的地方。 +赛题详细说明见赛题与数据.md。 + +--- + +## 第 4 页 + +## 2. Baseline 定位 +### 2.1 Baseline 概况 +本 baseline 是一个最小可运行基线(MVP)。 它的设计哲学是先把端到端流程跑通, +让参赛者能在 15 分钟内得到第一个可提交的 submission.csv,不追求高准确率。 +它能做的事: +- 读取 A 榜 100 题的 JSON 题目文件 +- 根据 doc_ids 加载已解析的 Markdown 文档 +- 将文档截断到 4000 字符,拼接为 prompt +- 调用 LLM 生成答案,并规范化输出格式 +- 并发处理 100 题,生成符合赛题要求的 JSON + CSV 结果 + +### 表格 1 + +| 核心信息 | 信息详情 | +| --- | --- | +| 赛题任务类型 | 金融长文本问答(选择题:单选/多选/判断) | +| baseline 代码 | https://github.com/li-xiu-qi/afac2026-financial-qa | +| baseline 涉及的库 | openai、pyyaml、pymupdf4llm | +| 所需环境和时间 | 环境:Python 3.10+、CPU
时间:15 分钟(含数据准备) | +| baseline 分数 | 准确率 16.62%(A 组 100 题),Token 消耗 353,032
(prompt 198,738 + completion 154,294) | +| baseline 所使用的方法 | 硬截断 + 单轮 LLM 调用(no-tool 模式) | +| 深入赛题会需要的知识点 | RAG 系统搭建、检索优化、Agent 决策、多轮推理 | + +--- + +## 第 5 页 + +为什么只用简单截断,不做检索 +很多参赛者第一反应是"文档太长,应该先搜再读"。这个思路本身没错,但过早引入检 +索会带来一系列连锁问题。核心矛盾在于:检索必然引入切片,而切片会破坏原文的 +连贯性。 +一段保险条款可能横跨三个自然段:前一段定义概念、中间一段给出公式、后一段附 +加例外条件。如果把这三段切成独立的片段分别检索,模型看到的可能只是"现金价值 += 累计保费 × 75%",却看不到后面紧跟着的"因意外伤害导致的退保除外"。这种连贯 +性断裂是检索方案的根本风险。 +从信息连贯性的角度看,全文 > 摘要 > 检索片段。全文让模型自己决定哪里重要,上 +下文天然完整;摘要虽然损失了细节,但至少保持了逻辑链条;检索片段则是最脆弱 +的,召回结果的质量直接决定了最终答案的质量,而单一检索策略(纯 BM25 或纯向 +量搜索)的召回质量往往不稳定。 +RAG 系统里之所以要设计"粗排 + 细排"的两阶段架构,正是因为粗排(BM25 或向量 +相似度)召回的结果中,经常夹杂着不相关或弱相关的内容,甚至因为词频巧合、 +embedding 漂移等各种原因把无关片段排得很靠前。细排(rerank)的作用就是做二 +次筛选,把真正相关的片段提上来。但在 baseline 阶段,我们没有足够的调优经验来 +确定阈值、去重策略和融合权重,贸然引入检索反而可能让模型面对一堆零散、重复、 +缺上下文的片段,理解成本比直接读截断全文还高。 +另外,检索不是加了就有效果的模块。它需要配合滑动窗口做片段边界保护、需要多 +路召回融合来互补 BM25 和向量搜索的盲区、需要动态阈值来过滤低质量片段。这些 +配套机制没有到位时,检索的提分潜力无法释放。 +当然,直接截断也有自己的问题(前半截可能刚好没有答案),但这属于"信息够不够" +的问题,比"信息对不对、连不连贯"更容易排查和迭代。baseline 阶段先固定一个稳定 +的上限,跑通端到端流程,后续再逐步引入检索并验证真实收益,是更务实的策略。 +实际得分验证:截断方案反而是最高分 +下图是我们团队在 A 榜的提交记录: + +--- + +## 第 6 页 + +硬截断 baseline 的得分与 Token 消耗: +我们也尝试过检索策略和混合策略的进阶方案,但得分均低于硬截断。一个可能的解 +释是,检索引入的切片破坏了原文连贯性,召回的片段中又混入了弱相关甚至不相关 +的内容,模型面对零散、缺上下文的片段,理解成本比直接读截断全文还高。另一个 +解释是,检索需要配合滑动窗口做片段边界保护、多路召回融合来互补盲区、动态阈 +值过滤低质量片段,这些配套机制没有到位时,检索的优势无法释放。 +本 baseline 选择硬截断,是为了在 baseline 阶段先把端到端流程跑通、建立一个稳定 +的得分基准,再逐步引入检索并验证每一步的真实收益。这并不否定检索的价值。 +一个参考基准:赛题裸跑是什么水平 +作为参照,赛题基于 Qwen-plus 做了一个最简单的基线:把长文档直接输入模型、不 +做检索也不做优化,A 组 100 题答对 17 道,准确率约 17%,Token 消耗约 362 万。 +这说明: +- 本赛题确实不容易,裸跑模型只有约 17% 的准确率 +- 本 baseline 的 16.62 分和这个裸跑基准处于同一水平,没有拖后腿 +- 从零到 17 分不需要什么技巧,但从 17 分往上提分,每一步都需要精心设计 +因此本 baseline 的策略是:先把端到端流程跑通,用一个稳定的基准来衡量后续改动 +的真实收益。如果你改了检索策略但分数没涨,至少知道不是基础流程的问题。 +它不做的事(留给后续版本): +- 没有检索模块,直接截断文档前半部分 +- 没有多轮推理,单轮提问、单轮回答 +- 没有证据链追溯,无法解释为什么选某个答案 +- 不做题目分类,所有题型用同一套 prompt + +### 表格 1 + +| 提交时间 | 方案 | 得分 | Token 消耗 | +| --- | --- | --- | --- | +| 21:54:50 | 硬截断 baseline | 16.62 | 353,032 | + +--- + +## 第 7 页 + +为什么选择 no-tool 而非 Function Calling +我们也测试过带 Function Calling(FC)的版本。FC 需要在每次请求时把工具声明 +(schema)放入 system prompt,让模型决定什么时候调用工具、传什么参数。实测 +A 组 100 题的 Token 消耗对比如下: +FC 的 completion tokens 确实略少(因为输出被结构化成了 JSON), 但 prompt +_tokens 多了近 40%。原因是每道题的 prompt 都要携带工具声明,而且模型需要先生 +成 tool call 的 JSON,再等待外部函数执行结果,再发起第二轮请求。整个流程的 +Token 开销和交互复杂度都更高。 +本 baseline 采用 no-tool 方案,用更少的 Token 完成同一批题目,流程也更简单:读 +文档、拼 prompt、直接出答案,没有中间状态。 + +### 表格 1 + +| 指标 | no-tool | tool(FC) | 差异 | +| --- | --- | --- | --- | +| prompt tokens | 198,738 | 273,226 | +37.5% | +| completion tokens | 154,294 | 142,726 | -7.5% | +| total tokens | 353,032 | 415,952 | +17.8% | + +--- + +## 第 8 页 + +## 3. 环境准备 +### 3.1 基础环境 +- Python 3.10+ +- 操作系统:Linux / WSL / macOS +### 3.2 安装依赖 +Bash +pip install -r requirements.txt +requirements.txt 中只包含 baseline 运行必需的依赖(openai 和 pyyaml)。 如果你需 +要重新解析 PDF,再额外安装 pymupdf4llm。 +### 3.3 数据准备 +data/ 目录已 gitignore,不纳入版本控制。你需要自行下载数据集并运行解析脚本生 +成。 +第一步:下载数据集 +## 1. 访问赛事页面:https://tianchi.aliyun.com/competition/entrance/532486 +## 2. 在「赛题与数据」栏目下载 public_dataset_a.zip(约 274MB) +## 3. 将压缩包放到项目根目录的 data/ 下 +第二步:运行解析脚本 +Bash +cd /home/ke/projects/afac2026-financial-qa +python -m src.preprocess.prepare_data +该脚本会自动完成两件事: +## 1. 解压 data/public_dataset_a.zip 到 data/raw_dataset/,得到题目 JSON +和原始 PDF +## 2. 调用 pymupdf4llm 把 86 份 PDF 逐页转成 Markdown,输出到 +data/processed_pymupdf4llm/ + +--- + +## 第 9 页 + +解析后的目录结构: +Plaintext +data/ +├── public_dataset_a.zip # 赛方原始压缩包 +├── raw_dataset/ # 解压后的原始数据 +│ ├── questions/group_a/ # A 组 100 道题目 JSON +│ └── raw/ # 86 份原始 PDF +└── processed_pymupdf4llm/ # 解析产物(每页一个 page_XXXX.md) +├── insurance/1/page_0001.md +├── regulatory/1/page_0001.md +└── ... +每篇文档被切分为多个 page_XXXX.md,按文件名排序后拼接即为完整文本。doc_id +(如 1、2)与题目 JSON 中的 doc_ids 一一对应。 +### 3.4 配置 API Key +获取 API Key +## 1. 登录阿里云百炼控制台:https://bailian.console.aliyun.com/ +## 2. 进入左侧导航栏 API Key 页面(或直接访问 +https://bailian.console.aliyun.com/?tab=model#/api-key) +## 3. 单击创建 API Key,选择默认业务空间,权限选全部 +## 4. 创建成功后立即复制并保存完整的 Key(关闭弹窗后无法再次查看明文) + +--- + +## 第 10 页 + +新用户有 100 万 Token 免费额度,有效期 90 天。 +配置到项目 +在 .env 文件中写入: +Bash +DASHSCOPE_API_KEY=sk-your-key-here +本 baseline 使用阿里云百炼平台(DashScope)的 Qwen 系列模型,接口兼容 +OpenAI SDK。详细接入说明见 design-draft/general/reference/阿里云百炼- +Qwen-API-接入笔记.md。 +接入其他模型(实验用途) +初赛阶段允许使用其他模型做实验。代码底层通过 OpenAI SDK 调用,只要 API 兼容 +OpenAI 格式就能接入。修改方式有两种: +方式一:通过 .env 文件(推荐) +Bash +DASHSCOPE_API_KEY=sk-your-key-here +# 切换模型(可选,默认 qwen-plus) +# MODEL_NAME=qwen-turbo +# 切换平台(实验用途,正式提交必须回退到百炼) +# API_BASE_URL=https://api.openai.com/v1 +代码读取优先级:环境变量 > config.yaml > 默认值。也就是说,如果你在 .env 中设 +置了 MODEL_NAME 和 API_BASE_URL,代码会优先使用环境变量;如果没设置,回退 +到 config.yaml;如果 config.yaml 也没改,使用默认值(百炼平台 + qwen-plus)。 + +--- + +## 第 11 页 + +方式二:通过 config.yaml +YAML +model: +name: "gpt-4o" # 或 deepseek-chat、claude-3-sonnet 等 +api_base: "https://api.openai.com/v1" +注意:正式提交必须使用 Qwen 系列模型,其他模型仅供实验调试用。 +### 3.5 快速运行(5 分钟上手) +配置完成后,运行以下命令即可得到第一个可提交的 submission.csv: +Bash +cd /home/ke/projects/afac2026-financial-qa +python -m src.agent.run --split A --limit 5 --workers 4 +参数说明: +- --split A:运行 A 组题目 +- --limit 5:只处理前 5 道题(适合快速验证),其余 95 题自动按题型规则随机 +填充 +- --workers 4:并发线程数 +运行结束后在 output/ 目录下生成: +- results_a.json:每道题的 qid、answer、Token 消耗明细 +- submission_a.csv:符合赛题提交要求的 CSV 文件 +预期表现:5 道题约 1-2 分钟跑完。如果这步跑通了,说明环境和 API Key 都配置正 +确,可以继续往下阅读核心模块的原理。 + +--- + +## 第 12 页 + +## 4. 项目结构 +Plaintext +src/ +├── agent/ +│ ├── agent.py # Agent 核心逻辑 +│ └── run.py # CLI 入口(并发执行) +├── evaluation/ +│ └── evaluator.py # 答案评测 + 提交文件生成 +├── utils/ +│ ├── llm_client.py # 双渠道 LLM 客户端 +│ └── helpers.py # 工具函数(配置加载、答案规范化等) +└── __init__.py +总代码量约 900 行,全部在 src/ 目录下,没有外部索引依赖,纯 Python 实现。 +## 5. 核心模块详解 +如果你已经通过 4.5 快速运行跑通了 baseline,本节将详细介绍每个模块的设计思路 +和代码实现。如果你还没跑通,建议先回到 4.5 完成上手验证,再阅读本节。 +### 5.1 数据层 +题目加载 +run.py 中的 load_questions() 遍历 *_questions.json,按 split 字段过滤出 A +组或 B 组题目: +```python +def load_questions(questions_dir: Path, split: str) -> list: +questions = [] +for domain_file in +sorted(questions_dir.glob("*_questions.json")): +data = load_json(domain_file) +for q in data: +if q.get("split", "").upper() == split.upper(): +questions.append(q) +questions.sort(key=lambda x: x["qid"]) +return questions +``` + +--- + +## 第 13 页 + +单条题目结构: +JSON +{ +"qid": "ins_a_001", +"domain": "insurance", +"split": "A", +"question": "关于四个养老保险...", +"options": {"A": "...", "B": "...", "C": "...", "D": "..."}, +"answer_format": "mcq", +"doc_ids": ["1", "2", "15", "16"] +} +文档读取 +FinancialQAAgent._read_document() 按 domain/doc_id/ 路径查找 page_*.md, +排序后拼接: +```python +def _read_document(self, domain: str, doc_id: str) -> str: +for root in [dataset_dir / "processed_pymupdf4llm", ...]: +parsed_dir = root / domain / str(doc_id) +if parsed_dir.exists(): +pages = sorted(parsed_dir.glob("page_*.md")) +return "\n\n".join(p.read_text(encoding="utf-8") for p +in pages) +``` +return f"[文档 {doc_id} 未找到]" +这里做了路径容错:优先从 config.yaml 指定的路径查找,回退到相对路径 +data/processed_pymupdf4llm。 + +--- + +## 第 14 页 + +### 5.2 Agent 核心 +ContextManager:简单截断 +```python +class ContextManager: +def __init__(self, max_chars: int = 320000, max_doc_chars: int += 4000): +``` +self.max_chars = max_chars # 总 prompt 上限 +self.max_doc_chars = max_doc_chars # 单篇文档上限 +def truncate_doc(self, text: str) -> str: +if len(text) <= self.max_doc_chars: +return text +return text[:self.max_doc_chars] + "\n\n[文档后续内容已省 +略]" +max_doc_chars=4000 是一个经验值。保险/监管文档通常 5000-8000 字符,截断到 +4000 基本能覆盖核心条款;财报/研报动辄 10 万字符以上,4000 字符只能覆盖目录 +和前言,这是本 baseline 最大的短板。 + +--- + +## 第 15 页 + +PromptBuilder:固定模板 +```python +class PromptBuilder: +def build_prompt(self, question, evidence, context_manager): +# 每篇文档单独截断后拼接 +context_parts = [] +for ev in evidence: +content = context_manager.truncate_doc(ev.content) +``` +context_parts.append(f"【文档 {ev.doc_id}】 +\n{content}") +context = "\n\n".join(context_parts) +options_text = "\n".join( +[f"{k}. {v}" for k, v in question.get("options", +{}).items()] +) +prompt = ( +"你是一位金融文档分析专家。请根据以下提供的文档内容,回答问 +题。\n" +"要求:\n" +"1. 仔细阅读文档中的相关条款、数据和事实\n" +"2. 对每个选项进行独立判断\n" +"3. 只输出最终答案字母,不要解释过程\n" +"4. 多选题答案按字母顺序排列,不加分隔符\n\n" +f"{context}\n\n" +f"问题:{question['question']}\n\n" +f"选项:\n{options_text}\n\n" +f"答案:" +) +return prompt +这个 prompt 的设计思路: +- 明确角色(金融文档分析专家),让模型进入"严肃阅读"模式 +- 要求"只输出字母",减少后处理难度 +- 多选题要求"按字母顺序排列",避免 "AC" 和 "CA" 被判定为不同答案 + +--- + +## 第 16 页 + +AnswerValidator:答案格式校验 +```python +class AnswerValidator: +@staticmethod +def validate_mcq(answer: str) -> bool: +return answer in {"A", "B", "C", "D"} +@staticmethod +def validate_multi(answer: str) -> bool: +return all(c in "ABCD" for c in answer) and answer == +"".join(sorted(answer)) and answer != "" +@staticmethod +def validate_tf(answer: str) -> bool: +return answer in {"A", "B"} +``` +校验规则直接对应赛题三种题型。multi 要求字母去重排序,所以 "ABBC" 会被规范 +化为 "ABC"。 + +--- + +## 第 17 页 + +FinancialQAAgent:主流程编排 +```python +def answer_question(self, question) -> Tuple[str, List[Evidence], +Dict]: +# 1. 加载证据(读取 doc_ids 对应的文档) +evidence = self._load_evidence(question) +# 2. 构建 prompt(截断 + 拼接) +prompt = self.prompt_builder.build_prompt(question, evidence, +self.context_manager) +prompt = self.context_manager.truncate(prompt) +# 3. 调用 LLM +messages = [{"role": "user", "content": prompt}] +response = self.llm.chat(messages, max_tokens=4096) +# 4. 重试机制:内容被过滤或输出为空时,缩短文档再试 +retry_count = 0 +while (response.finish_reason in ("content_filter", "length") +or not response.content.strip()) and retry_count < 2: +self.context_manager.max_doc_chars //= 2 +``` +... # 重新构建 prompt 并调用 +# 5. 解析答案 +answer = self._parse_answer(response, +question.get("answer_format", "mcq")) +return answer, evidence, token_usage +主流程非常直接:读文档、拼 prompt、问模型、取答案。重试机制是本方案唯一的"鲁 +棒性"设计:当模型因内容过长触发过滤或输出为空时,自动将单篇截断长度减半 +(4000 -> 2000 -> 1000),最多重试 2 次。 + +--- + +## 第 18 页 + +### 5.3 LLM 调用层 +llm_client.py 封装了双渠道调用策略(均在百炼平台内): +```python +class LLMClient: +def __init__(self, api_key, base_url, model, temperature): +# 主渠道:qwen-plus(性价比高,文档分析能力强) +self.router_client = OpenAI( +api_key=api_key, +base_url="https://dashscope.aliyuncs.com/compatible- +mode/v1", +) +self.router_model = "qwen-plus" +# 回退渠道:qwen-turbo(同一平台内的备用模型) +self.fallback_client = OpenAI( +api_key=api_key, +base_url="https://dashscope.aliyuncs.com/compatible- +mode/v1", +) +self.fallback_model = "qwen-turbo" +``` +触发回退的条件: +- HTTP 429(限流) +- HTTP 5xx(服务端错误) +- 网络连接超时 +双渠道策略本质上是一种冗余/容错设计。主模型因限流或服务端异常不可用时,自动 +切换到备用模型继续服务。这种设计在跨服务商场景下价值更大(不同服务商的审查 +机制、限流策略、可用性互相独立),但本次比赛限定使用阿里云百炼,所以回退只是 +同一平台内不同模型之间的切换。代码里保留这个结构是为了展示"回退设计"这一工程 +思维,方便读者学习。实际跑 A 组 100 题时,约 5-10% 的题目会触发回退。 +注意: 百炼平台的 Qwen 系列模型支持 enable_thinking 参数(通过 extra_body +传入),用于控制是否开启思考模式。金融选择题不是复杂推理任务,开启思考会额外 +消耗大量 completion token 且对准确率帮助有限,因此代码中显式设置为 False 关闭。 +reasoning_effort 是 DeepSeek 系列特有的参数,Qwen 不支持。 +相关文档: +- 深度思考模型用法(含 enable_thinking 参数说明): +https://help.aliyun.com/zh/model-studio/deep-thinking + +--- + +## 第 19 页 + +- DashScope API 参考:https://help.aliyun.com/zh/model-studio/qwen-api-via- +dashscope +### 5.4 评测与提交层 +evaluator.py 包含两部分: +Evaluator:如果有标准答案,可以计算准确率和 Token 效率分。 +```python +def compute_final_score(self, accuracy: float, total_tokens: int) +-> float: +token_score = max(0.0, min(1.0, (self.token_budget - +total_tokens) / self.token_budget)) +return 100.0 * accuracy * (0.7 + 0.3 * token_score) +``` +公式含义:准确率占 70% 权重,Token 效率占 30% 权重。在 500 万 Token 预算内, +消耗越少得分越高。 +SubmissionGenerator:生成赛题要求的提交文件。 +```python +def generate_csv(self, answers, token_stats) -> Path: +with open(path, "w", newline="", encoding="utf-8") as f: +writer = csv.writer(f) +writer.writerow(["qid", "answer", "prompt_tokens", +"completion_tokens", "total_tokens"]) +# 第一行是汇总 +writer.writerow(["summary", "", total_prompt, +total_completion, total_tokens]) +# 随后每行一题 +for qid in sorted(answers.keys()): +... +``` +CSV 第一行必须是 summary,汇总全量 Token 消耗,否则评测脚本会报错。 + +--- + +## 第 20 页 + +## 6. 运行方式 +### 6.1 命令行运行 +Bash +cd /home/ke/projects/afac2026-financial-qa +python -m src.agent.run --split A --workers 8 +参数说明: +- --split A:运行 A 组题目 +- --workers 8:并发线程数(百炼 API 并发限制约 10,留 2 个余量) +- --output results_a.json:可选,自定义输出路径 +- --limit 10:可选,只处理前 10 道题,其余随机填充。适合快速验证流程或测 +试 API 连通性 +快速测试示例(只跑 5 题,约 1-2 分钟): +Bash +python -m src.agent.run --split A --limit 5 --workers 4 +未处理的 95 题会自动填充随机答案(单选/多选/判断分别按题型规则生成),生成的 +submission_a.csv 格式完整,可直接提交到评测系统查看格式是否通过。 +### 6.2 输出文件 +运行结束后在 output/ 目录下生成: +- results_a.json:每道题的 qid、answer、各阶段 Token 消耗 +- submission_a.csv:符合赛题提交要求的 CSV 文件 +### 6.3 预期表现 +- 成功率:100%(100 题全部有输出) +- Token 消耗:约 36-40 万 prompt tokens + 10-15 万 completion tokens +- 准确率:未知(没有标准答案库时无法计算) +- 答案分布:A 选项偏多(约 36/100),可能存在位置偏差 + +--- + +## 第 21 页 + +## 7. 局限与优化路线图 +本 baseline 的每一个局限都对应一条清晰的优化路径。但在动手优化前,需要建立一 +个正确预期:本次赛题的准确率并不容易提升。 +原因有三: +## 1. 题型对模型综合能力要求高。一道题可能同时涉及跨文档阅读(4 份条款)、公式 +计算(提取参数后代入)、逻辑推理(判断例外情形是否适用)和精细比较(排序或判 +断正误)。这意味着模型不仅要"读到"信息,还要"算对""想通""比准"。 +## 2. 金融知识本身门槛高。保险条款中的"等待期""免赔额""责任免除",监管法规中的" +受益所有人识别""客户尽职调查",这些概念对非专业人士来说理解成本很高。模型如 +果没有足够的金融领域预训练知识,光凭 prompt 很难做到精准理解。 +## 3. 优化方案往往有副作用。比如引入检索可以提升长文档覆盖率,但可能漏掉关键 +否定词;让模型做详细推理可以提升判断准确性,但会大幅增加 Token 消耗。每改一 +个地方,可能解决一个问题,又引入另一个问题。 +因此,不要期望某个单一技巧就能让准确率大幅提升。真实的提分路径是多模块协同 +优化:文档解析质量 + 检索质量 + 上下文管理(动态记忆压缩)+ prompt 设计 + 答案 +检查,层层叠加才能看到效果。 +与官方优化方向的对照 +赛题官方在 baseline 说明中明确列出了五个重点优化方向,和我们的思路基本一致: + +### 表格 1 + +| 官方方向 | 对应本教程的模块 | +| --- | --- | +| 文档解析质量:提升 PDF 文本抽取、表
格处理和版面结构还原能力 | 8.1 PDF 解析质量 | +| 检索策略:构建面向金融术语、条款编
号和指标名称的混合检索 | 8.2 上下文管理粗放(含检索层) | +| 证据聚合:支持同题多文档、多段落证
据的联合判断 | 8.2 上下文管理(片段组装) | +| 答案约束:对模型输出进行字母抽取、
排序、去重和合法性检查 | 8.3 单轮推理无自检、答案检查薄弱 | +| 领域知识增强:针对保险、监管、合
同、财报、研报分别设计提示词 | 8.4 Prompt 简陋(领域特化)+ 8.5 无
结构化知识提取 | + +--- + +## 第 22 页 + +其中最容易被忽略的是文档解析质量。如果 PDF 解析阶段就把表格拆散了、数字漏掉 +了、段落顺序搞错了,后续无论检索多精准、推理多严密,都是建立在错误信息上的。 +赛题官方也明确将"提升 PDF 文本抽取、表格处理和版面结构还原能力"列为重点优化 +方向之一。 +### 7.1 PDF 解析质量 +本 baseline 使用 pymupdf4llm 将 PDF 转为 Markdown,解析质量直接决定了后续所 +有环节的上限。如果解析阶段出现以下问题,准确率再高也救不回来: +- 表格丢失或错位:财报中的三大报表如果行列对应关系被破坏,模型读到的"营业 +收入"和"净利润"可能是同一列的数据,计算题直接全军覆没 +- 数字截断或变形:条款中的金额、比例、年限等关键数字如果解析出错(比如 +"100,000"变成"100"),公式计算的结果必然错误 +- 段落顺序错乱:保险条款中"等待期"的定义和"例外情形"如果不在同一段落附近, +模型可能只看到前半句"等待期内不赔",漏掉后半句"因意外伤害除外" +- 标题层级丢失:监管法规的"第一章总则""第二章适用范围"等层级结构如果被打 +平成纯文本,模型难以判断条款的适用优先级 +优化方向: +- 多解析器对比:同一篇 PDF 用 pymupdf4llm、Marker、MinerU 等多种工具解析, +取质量最好或互补性最强的一份 +- 表格专项处理:对财报/合同中的表格使用专门的表格解析工具(如 camelot、 +tabula),保留行列结构, M转arkd成own 表格或 CSV +- 版面结构还原:在 Markdown 中保留页码、章节标题、条款编号等结构信息(如 +# 第一章保险责任、**第 3.2 条等待期**),帮助模型快速定位 +- 解析后校验:用规则或 LLM 检查解析结果是否存在明显异常(如连续 20 行都是 +乱码、表格列数不一致、金额数字明显缺失),异常时切换备用解析器 +需要特别注意的是:PDF 解析是一次性投入。A 榜 86 份 PDF 解析一次后可以复用, +不受 500 万 Token 总预算限制。在解析阶段投入精力换取更高质量的输入文本,性价 +比远高于在答题阶段用更多 Token 去弥补解析错误。 + +--- + +## 第 23 页 + +### 7.2 上下文管理粗放(文档截断粗暴) +本 baseline 保留文档前 4000 字符,对于财报/研报/合同来说命中关键信息的概率很低。 +这是上下文管理问题的一个具体表现:系统对"保留什么、丢弃什么、怎么拼接"没有任 +何策略,只是机械地截断。优化方向分两层: +(1)文本切片 + BM25 +先把文档切成固定长度或按段落切分,再用 BM25 算法计算每个片段与问题的相关性 +得分,取 top K 送给 LLM。优点是轻量、不依赖外部模型,jieba 分词 + rank-bm25 库 +就能跑通;缺点是只考虑词频匹配,无法理解语义(比如"退保费用"和"退保手续费"会 +被当成两个词)。 +(2)无索引关键词匹配 + 滚动窗口 +不建索引,直接在文档中搜索问题关键词,提取关键词所在位置的前后若干字符作为 +上下文。复现门槛最低,但召回质量波动大。 +对应实现方式: +- BM25 单次检索 top 5:用 jieba 分词、按领域差异化 chunk_size,轻量且可控 +- 无索引关键词匹配 + 滚动窗口:复现门槛更低,不依赖预建索引 +上下文管理的更高层次:片段组装与动态压缩 +即使有了检索,如何把搜到的片段拼成一份高质量的 prompt,仍然是上下文管理的核 +心问题: +- 片段去重与边界修复:检索返回的 top K 片段可能存在重叠(比如 chunk1 结尾和 +chunk2 开头重复了 200 字),直接拼接会造成内容冗余,浪费 Token。需要去重并保 +证段落边界完整。 +- 信息密度排序:检索结果不是全部值得放入 prompt。按相关性得分排序后,设定 +一个阈值,只保留得分明显高于阈值的片段,把有限的上下文窗口留给真正相关的信 +息。 +- 多轮上下文传递:迭代检索时,第一轮搜到的关键信息要在第二轮 prompt 中保留, +避免"搜到了又忘了"。但同时要控制历史信息的累积长度,防止 prompt 无限膨胀。 +- 按题型动态调整上下文长度:简单的事实查找题可能只需要 2000 字的上下文;跨 +文档对比题可能需要 4 篇文档各取 top 3 片段,总共上万字;计算题则需要确保公式 +所在的段落不被截断。上下文长度不应是固定值,而应根据题目复杂度自适应调整。 + +--- + +## 第 24 页 + +### 7.3 单轮推理无自检、答案检查薄弱 +本 baseline 只做单轮调用,模型给出答案后不做二次核验,对错全凭一次输出。而且 +答案检查仅限于格式校验(是不是合法字母),不做内容层面的合理性判断。优化方向 +分两层: +(1)推理层:从单轮到多轮 +- 迭代检索:LLM 判断信息是否充分,不够则生成新查询继续搜 +- 自我纠错:让模型回顾自己的答案,检查是否与文档证据矛盾。比如模型选了 A, +但文档明确说"以下情形不属于保险责任",自我纠错环节可以发现这个冲突 +- 多选题逐项验证:多选题让每个选项单独过一遍"文档是否支持",比一次性让模型 +选所有答案更不容易漏选 +(2)答案检查层:从格式校验到内容校验 +当前 baseline 的 AnswerValidator 只检查答案格式(mcq 是不是单个字母、multi 字 +母是否排序),这是最底层的检查。更高层的检查包括: +- 证据一致性校验:模型说答案是 A,但检索到的证据片段明确支持 B,系统应该能 +识别这个矛盾。实现方式可以是让模型在输出答案的同时给出判断依据,然后用另一 +轮调用验证"依据是否支持答案" +- 题型规则校验:判断题只有 A/B 两个选项,如果模型输出 C,直接判定为异常并 +fallback;多选题至少要有两个正确选项(按出题习惯),如果模型只选了一个,可以 +触发二次确认 +- 多轮一致性校验:同一道题用相同 prompt 调用两次,如果两次答案不同,说明模 +型对该题不确定,可以引入第三次调用取多数,或改为保守策略(如多选题在有争议 +时少选) +- 位置偏差修正:如果发现某次提交的答案 A 明显偏多(如 40% 以上),可以对 A +选项的题增加二次确认,或在 prompt 中加入"不要偏向第一个选项"的去偏指令 +- 默认值策略:模型输出为空或无法解析时,按题型规则 fallback。单选默认 A,判 +断默认 A,多选默认 AB(保守策略是选最少的可能组合) +### 7.4 Prompt 简陋 +本 baseline 用同一套 prompt 处理所有题型和领域。优化方向: +- 混合策略:短文档全文读取、长文档检索,按文档长度动态选择策略 +- 领域特化:针对不同领域和题型设计专用提示词,加入去偏指令("不要偏向第一 +个选项") + +--- + +## 第 25 页 + +### 7.5 无结构化知识提取 +本 baseline 把文档当纯文本处理,模型每次回答都要重新阅读原始段落。如果题目要 +求比较 4 份保险条款的等待期、免赔额、退保费用,模型需要在 4 份文档中反复搜索 +同类信息,效率低且容易遗漏。 +优化方向:构建领域知识图谱 +知识图谱的核心价值在于把分散在各文档中的结构化信息抽取出来,统一存储为"实体- +关系-属性"三元组。例如从保险条款中提取: +Plaintext +(国寿增益宝, 等待期, 30 天) +(国寿增益宝, 免赔额, 10000 元) +(国寿增益宝, 第 7 年退保费用, 0%) +(平安智盈金生, 等待期, 90 天) +... +当题目问"以下哪款产品的等待期最短"时,系统不需要再去读原始文档,直接从图谱中 +按"等待期"属性排序即可。这种方式在跨文档聚合类题目上优势明显:信息已经结构化, +比较和推理的准确率远高于让模型在原始文本中自行定位。 +具体实现可以分两步: +## 1. 离线抽取:预处理阶段用 LLM 遍历所有文档,按领域定义的信息模板(保险条款 +提取"等待期/免赔额/退保费用/身故保险金",财报提取"营收/净利润/研发投入/现金流") +抽取结构化字段,存入 JSON 或图数据库 +## 2. 在线查询:答题时先判断题目是否涉及结构化比较,如果是则优先查知识图谱; +如果是需要原文引用的条款解释题,再回退到原始文本检索 +知识图谱的代价是预处理阶段需要消耗额外 Token 做信息抽取,但在答题阶段可以大 +幅减少重复阅读,整体 Token 效率可能更优。而且如果允许的话,离线抽取不受 500 +万 Token 总预算限制,性价比很高。不过赛题方是有对知识图谱构建的过程计算对应 +的 token 的,是否能满足我们的实际要求的话,还需要使用实战来进行测试了。 + +--- + +## 第 26 页 + +附录 A:核心参数速查 +本教程对应的源码位于项目根目录 src/ 下。进阶优化方向、题型分析和金融文档背 +景知识请参考进阶教程。 +参考文献 +[1] PyMuPDF4LLM: Convert PDF to Markdown via PyMuPDF. Artifex Software, 2024. +GitHub: pymupdf/PyMuPDF4LLM +[2] AFAC2026 赛道四:金融长文本 Agent 的动态记忆压缩与高效问答。赛方文档, +2026。参见 docs/赛题与数据.md、docs/赛事介绍.md +[3] 阿里云百炼 DashScope API 参考。参见 https://help.aliyun.com/zh/model-studio/ +[4] BM25 检索算法。Robertson & Walker, 1994. Okapi at TREC-3. 以及 Trotman, +Puurula & Burgess, 2014. Improvements to BM25 and Language Models Examined. +arXiv:1401.4737 + +### 表格 1 + +| 参数 | 默认值 | 说明 | +| --- | --- | --- | +| max_doc_chars | 4000 | 单篇文档截断长度 | +| max_chars | 320000 | 总 prompt 截断上限 | +| max_tokens | 4096 | LLM 最大输出长度 | +| temperature | 0.0 | 采样温度(greedy) | +| workers | 8 | 并发线程数 | +| enable_thinking | False | 关闭思考模式 | + +--- diff --git "a/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\350\265\233\351\242\230\345\233\233\357\274\232\350\241\214\344\270\232\345\230\211\345\256\276\345\210\206\344\272\253-\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\350\265\233\351\242\230\345\233\233\357\274\232\350\241\214\344\270\232\345\230\211\345\256\276\345\210\206\344\272\253-\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" new file mode 100644 index 0000000..744bb5f --- /dev/null +++ "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\350\265\233\351\242\230\345\233\233\357\274\232\350\241\214\344\270\232\345\230\211\345\256\276\345\210\206\344\272\253-\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224\346\214\221\346\210\230.md" @@ -0,0 +1,213 @@ +# 赛题四：行业嘉宾分享-金融长文本 Agent 的动态记忆压缩与高效问答挑战 + +> 来源：`赛题四：行业嘉宾分享-金融长文本 Agent 的动态记忆压缩与高效问答挑战.pdf` + +## 第 1 页 + +从"读文档"到"管理金融判断" +金融场景下的 Agent 系统设计与长文本管理 +作者:山鬼 + +--- + +## 第 2 页 + +金融行业的独特性 +作者:山鬼 +金融行业的独特性 +今天分享的主题是从"读文档"到"管理金融判断"。在进入技术之前,我想先聊一下金融行业的独特 +性,agent的价值目前对于行业来说,展现出的两个比较大的价值,一个是业务探索、另一个是场景 +落地,这些都会要了解这个行业是有哪些问题是落地中需要被解决的。这些问题决定了通用的 +Agent 方案为什么在金融场景里会失效。 +什么是金融长文本? +在我看来金融行业也是信息支撑的行业,所以都会面临长文本的问题,不过不同的是,金融领域的 +长文本有着一些独特的难点,这个主要是由于行业历史信息结构导致的。 +第一:证据分散,且有依赖顺序 +比如要回答"这个客户能不能再批一笔500万的短期贷款",需要的信息来自账务系统的余额、风 +控系统的还款记录、外部征信报告、内部授信政策条款。没有一份单独的材料能回答这个问题。而 +且这些证据之间有逻辑顺序——不能先看政策再看征信,因为客户的征信状态会影响哪条政策适 +用。这是结构性的问题。 +第二:切片断链,例外条款无法被语义检索命中 +比如政策第3页:"单笔短期贷款上限1000万。" 政策第17页:"但制造业客户近6个月出现逾期 +的,新增授信须降级审批。"这两条规则在业务逻辑上是强依赖的。但按512字切片之后,"单笔上限 +1000万"和"制造业客户逾期降级审批"在向量空间里的相似度并不高。RAG检索到了第3页,没有检 +索到第17页。信息链断开是一个较为常见的长文本中的问题。 +第三:例外条款的权重和主条款一样,但位置是隐蔽的 +有些文档有一个特点:正文写上限,另条写降级;产品说明书写收益,风险揭示书写亏损情 +形;主条款写赔付,附录写例外。这些配对内容在文档结构上是分离的,但在业务逻辑上是不可分 +割的。如果仅仅召回单个信息,最终的结论大概率会获得一个看起来逻辑正确的错误答案。 +第四:数据时效性与信息冲突 + +--- + +## 第 3 页 + +从业务场景到 Agent 系统设计 +金融数据有时效性。比如征信报告是上个月拉的,但客户昨天刚发生了一笔逾期。政策文档有 +新旧两个版本,检索可能同时返回,而且两个版本对同一情形的规定不同。更复杂的情况是信息冲 +突:客户在系统内登记的行业分类是"制造业",工商信息显示的是"贸易业"。两个数据来源给出了不 +同的输入,而哪个适用于当前判断,需要有人来确认。通用场景里,信息冲突可能是噪音。金融场 +景里,信息冲突是一个需要明确处理、不能自动决策的问题。 +第五:口径不一致 +同一个指标,在不同产品、不同年份、不同主体下含义可能不同。"营业收入"在并表口径和母公 +司口径下数值可能差出数倍。"等待期"在不同险种里的起算方式不同。"逾期"在不同信贷产品里的认 +定标准不同。Agent必须知道不同来源的数据能不能放在一起比较,不能把不同口径的数字直接拼 +在一张对比表里。 +第六:监管留痕与责任归属 +金融场景有一个通用场景里几乎没有的要求:事后可以说清楚这个结论是怎么得出来的。审计 +要求、监管检查、客户投诉,都可能需要还原推理过程。"模型说可以所以我批了"在金融行业不是一 +个可以接受的解释。这个要求直接影响系统设计:中间推理步骤不能随便压缩,证据来源必须保留 +原文和页码,每一个影响最终结论的判断步骤必须可以被追溯。 +从业务场景到 Agent 系统设计 +我们来看一下场景中的agent是如何运作的。当客户经理问"这个客户能不能再批一笔500万的短期贷 +款"。这个问题看起来是一个查询,但真正开始想怎么让 Agent 回答它的时候,会遇到四个设计上的 +问题需要决策。 +Agent 需要去哪里取信息,顺序是什么? +要回答这个问题,Agent 需要:账务余额、还款记录、征信报告、政策条款。最直接的做法是 +按顺序调用不同的数据接口。但很快会发现这里的问题:政策条款的检索需要知道客户是什么行 +业、有没有逾期,而这两条信息要从前面的工具调用里拿。如果顺序写死,就可能在信息还没拿到 +的情况下去做检索,检索的查询词是不完整的。 +所以系统的第一个设计问题是:任务之间有依赖关系,不能顺序执行,需要先建依赖图。 + +--- + +## 第 4 页 + +从业务场景到 Agent 系统设计 +查询账务余额 +用户输入:能否批500万贷款意图解析 + 任务拆解查询还款记录整合客户状态构造针对性检索查询检索政策条款多跳推理验证步骤输出结论 + 待确认项 +拉取征信报告 +图1:贷款审批信息依赖图 +这张图里有一个关键节点:F,整合客户状态。在这个节点之前,三个工具调用可以并行;这个 +节点之后,检索才正式开始。检索的查询词也不是从用户原始问题直接生成的,而是会从已经确认 +的客户状态结合初始信息合成的。这就是为什么这道题的检索要做多轮——第一轮用原始问题找主 +条款,后续几轮用客户的具体情况找例外和降级规则。 +取回来的信息,怎么放进上下文? +现在工具调用完成了,Agent 手里有: +账务系统返回的余额明细(数值型) +风控系统返回的还款记录(数值 + 状态) +征信报告(结构化文本,几十条记录) +政策条款检索结果(非结构化文本,多个片段) +最简单的做法:全部放进上下文。问题是上下文窗口有限,而且这四类信息的性质完全不同 +——有些信息不能动,有些可以整理,有些可以压缩。如果统一处理,要么窗口很快耗尽,要么压 +缩了不该压缩的内容。 +所以系统的第二个设计问题是:不同性质的信息,需要不同的处理策略。 + +--- + +## 第 5 页 + +从业务场景到 Agent 系统设计 +数值/状态数据完整保留原始数据 +文档检索结果格式整理 + 保留出处 +工具调用结果按信息性质分类 +元信息可以压缩 +冲突数据打冲突标注等待确认 +图2:信息分类与处理策略 +这里有一个在通用场景里比较少见的问题:冲突数据。如客户在系统内登记的行业分类是制造 +业,但工商信息里显示的是贸易业。两个数据来源,两个不同的值,而这个值直接决定哪条政策适 +用。通用 Agent 的做法通常是取较新的数据,或者摘要成"行业分类存在不一致"然后继续推理。但 +在金融场景里,这个冲突不能被 Agent 自动决策——它需要被打标、被输出、等待人工确认。这就 +是 F 这个分支的逻辑。 +遇到不确定的信息,Agent 继续推理还是暂停 +现在我们有了一条打了冲突标注的数据。系统面临一个选择:忽略冲突继续推理,还是在这个 +节点暂停,把问题抛给用户?这是金融 Agent 系统里较为容易忽视的一个决策点。如果继续推理, +Agent 会基于不确定的前提给出一个"有说服力的错误"——答案看起来完整,但建立在一个未经确 +认的假设上。 +所以系统的第三个设计问题是:不确定性的处理机制:什么时候继续,什么时候暂停,暂停之 +后怎么恢复。 + +--- + +## 第 6 页 + +从业务场景到 Agent 系统设计 +否继续推理 +推理过程发现不确定项? 进入验证步骤 +低标注存疑继续推理输出时附注 +是不确定性等级? +高暂停推理生成待确认清单输出已确认部分 + 待确认项 + 条件结论等待用户输入用户确认后恢复继续推理 +图3:不确定性处理机制 +这里有一个体验工程的细节:暂停之后要能恢复。Agent 不是从头重新跑,而是从状态层里拿 +到已经确认的信息,只补充用户刚刚确认的那一条,然后从中断的节点继续。这要求状态层的设计 +必须支持"任务进度记录",知道推理走到了哪一步、哪些前提已经确认、哪些还有缺失。 +上下文快满了,保留什么、丢弃什么? +推理过程走了几步之后,上下文里已经有了:工具调用结果、政策条款片段、CoT 推理步骤、 +用户的历史输入。这时候上下文接近上限了。最简单的做法:截断最早的内容。问题是最早的内容 +可能是证据层的关键条款——截断了,推理结论就失去了依据,而且事后审计的时候说不清楚这个 +结论从哪来的。 +所以系统的第四个设计问题是:上下文不够用时,按优先级而不是按时间顺序来决定保留什 +么。 + +--- + +## 第 7 页 + +从业务场景到 Agent 系统设计 +证据层不压缩 +状态层不压缩 +推理层决策节点不压缩 +上下文接近Token上限按优先级排序 +工具层数值数据不压缩 +推理层探索步骤 Observation Masking +工具层元信息可以摘要或删除 +图4:上下文优先级压缩策略 +市场上的主流做法是 LLM 摘要,让模型把历史内容总结一遍,替换掉原始内容。Cursor、 +OpenHands 等工具都曾用这个思路。NeurIPS 2025 有一篇 JetBrains Research 的论文"The +Complexity Trap"做了系统对比,发现Observation Masking——直接把旧的工具输出替换成占 +位符,保留推理历史——在成本上比原始 Agent 降低约 50%,效果和 LLM 摘要基本持平,有时候 +还略优。 +推理历史保留,工具输出按优先级 Masking——这也和我们分层设计的逻辑是一致的。区别是 +金融场景需要额外明确哪些工具输出属于"不能被 Mask 的数值数据"。 +整体系统视图 +把四个设计决策放在一起,就可以看出一套agent 的雏形体系。 + +--- + +## 第 8 页 + +金融长文本的复杂性 +证据层不压缩 +无或低不确定性否 +用户输入意图解析 + 依赖图构建并行工具调用信息分类状态层结构化存储发现不确定性? 多跳推理 Token接近上限? 验证步骤输出结论 + 依据 + 待确认项 +高不确定性暂停 + 输出待确认项等待用户确认从状态层恢复是按优先级压缩 +冲突层打标等待确认 +图5:整体系统视图 +金融长文本的复杂性 +搭建完系统后再再来看金融长文本的复杂性,金融场景里真正的挑战不是文档长,是信息结构 +复杂。容量问题是可以用资源换的——换更大的上下文窗口,或者更高效的检索。但信息结构的问 +题是换不了的:跨文档的逻辑依赖、非线性的条款结构、多源头数据之间的冲突,这些问题不会因 +为上下文窗口变大而消失。 +如何管理好上下文、信息以什么顺序进、在什么时候退出。这是一个主动管理的问题,不是容 +量问题。 +现在回头看,前一个模块里展示的四层信息结构——证据层、状态层、推理层、工具层——看 +起来很整齐。但并不是一开始就存在的,大多数的时候,我们能接触到的更多是一个通用的解决方 +案,它覆盖了流程上的最小路径,当真正在现实业务中运转时,会发现大量的异常case。 +比如:早期版本里,Agent检索到政策条款之后,会让模型先总结一遍,把几段相关的条款整 +合成一段话,再放进上下文。理由是这样更紧凑,节省token。结果是:当推理结论出来、我们想验 +证依据的时候,发现存进上下文的是"政策要求制造业客户逾期后须降级审批",但不知道这句话来自 +哪份文档的第几页,也不知道原话是什么。事后追溯不了。 +这个失败让我们意识到证据层的内容必须保留原文和出处,不能做任何形式的内容压缩。系统 +在运行的过程中不断的吸收case补充系统边界,但是仍无法保障一定可以覆盖全量的用户场景,所 +以如何思考系统边界与边界之外的常用应该如何应对。 +边界与应用场景 +最后,宏观来看一下这类系统的边界在哪。 +此类技术路线的场景,有一个共同结构:答案不在一个地方,需要从多份材料里建立证据链, +而且对例外条款的识别会直接影响结论。具体到金融行业,这五类场景都符合这个结构:投研分 +析、合规审查、客服理赔、合同审查、风控尽调。 + +--- + +## 第 9 页 + +边界与应用场景 +边界在哪:凡是需要承担最终结论责任的判断,Agent做的是辅助,不是决策。授信审批需要 +有人签字,理赔核赔需要有人负责,合规意见需要合规人员出具。这不是因为技术不够成熟,是因 +为金融行业的责任结构决定的——责任在人,不在系统。 +所以设计这类系统的时候,有一件事比技术选型更重要:你要先想清楚,哪些输出是Agent可 +以直接给的,哪些必须走人工确认,哪些是在任何情况下都不应该由Agent下结论的。把这条线划 +清楚,系统才真正能用。 +我们需要思考的问题是如何来去构建在金融任务中的信息状态,才可以让AI在处理任务的时候 +可以获取足够完善的信息,并且可以做好token的管理,避免出现过度的token消耗。 + +--- diff --git "a/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224-\345\233\276\350\241\250\345\242\236\345\274\272\347\211\210.md" "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224-\345\233\276\350\241\250\345\242\236\345\274\272\347\211\210.md" new file mode 100644 index 0000000..f405790 --- /dev/null +++ "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224-\345\233\276\350\241\250\345\242\236\345\274\272\347\211\210.md" @@ -0,0 +1,969 @@ +# 金融长文本 Agent 的动态记忆压缩与高效问答 + +> 来源：`金融长文本 Agent 的动态记忆压缩与高效问答.pdf` +> +> 注：原 PDF 的内嵌字体编码异常，正文采用中文 OCR 提取；图表中的少量字符可能存在识别误差。 + +## 图表增强说明 + +以下 Mermaid 图和 Markdown 表格是根据原 PDF 对应页面重绘的语义版，用于检索、复制和理解流程；原始配色、图标、柱状图/折线图和二维码仍以 PDF 为准。 + +## 第 1 页 + +AFAC2026 挑战组赛题四 +Financial Long-Text Agent Challenge +TECH SHARE +金融长文本Agent的动态记忆压缩与高效问 +答 +Dynamic Memory Compression & Efficient QA for Financial Long-Text Agent +李秀奇(筱可) +Datawhale 成员 |AFAC2026 赛道 baseline 教程作者 + +--- + +## 第 2 页 + +目录 +TABLE OF CONTENTS +CONTENTS +01 +题目背景与解析 +Background and Problem Analysis +02 +数据理解与预处理 +Data Understanding and Preprocessing +03 +题型分类与问答策略 +Question Classification and QA Strategies +04 +Agent 上下文管理 +Agent Context Management +05 +总结与核心判断 +Summary and Key Insights +- 1.1 赛事概览与核心约束 +- 1.2 评测公式与 Token 策略 +- 1.3 输入输出格式 +- 1.4 Baseline 整体流程 +- 1.5 Baseline Token 消耗分析 +- 2.1 五大金融文档领域与题型特征 +- 2.2 PDF 解析方案与检索策略 +- 2.3 数据预处理管线 +.3.1题型分类与路由策略 +- 3.2 检索策略与提示词优化 +- 3.3计算题处理与答案自检 +- 4.1从提示词工程到上下文工程 +- 4.2 记忆系统架构与压缩策略 +. 4.3 Token 预算与答题策略 +- 5.1总结与核心判断 + +--- + +## 第 3 页 + +## Chapter One +题目背景与解析 +Background and Problem Analysis +1.1 +1.2 +赛事概览与核心约束 +评测公式与 Token 投入策略 +〇1 + +--- + +## 第 4 页 + +01题目背景与解析/1.1赛事概览与核心约束 +04 +赛事概览与核心约束 +赛题核心设定 +- 数据:200题,五大金融领域 +.基座模型:仅限 Qwen 系列,禁止微调/reasoning +.禁止 Embedding 和 Rerank 模型(经赛题方确认) +- Token 预算:全部接口调用总和≤500 万 +- 评测:准确率70%+Token 效率30% +核心挑战 +A榜vS B榜差异 +A 榜提供 doc_ids,B榜需自行检索定位 +- Datawhale +AFACO G +金融智能创新大赛 +关键数据 +200 +总题目数 +500万 +Token 预算上限 +无向量检索 +Embedding/Rerank 不可用,退回规则方法 +五类文档差异巨大 +保险/法规/合同/报表/研报,结构各异 +核心洞察:多数agent 的失败,根源在上下文工程的失败,而非模型能力的不足。给定固定模型和token 预算,怎么让模型读最少但最对的信息,是关键问题。 +领域 +保险条款 +监管法规 +金融合同 +财务报表 +行业研报 +文档数 +16 +26 +14 +10 +20 +题目数 +20 +20 +20 +20 +20 +核心考查点 +保险金计算、退保金额、等待期、免责条款 +法条引用、合规义务、跨文档对比 +债券条款、发行信息、信用评级条件 +年报指标、经营现金流、研发投入 +趋势判断、公司对比、数据核验 + + +### 图表重绘：五大领域分布 + +| 领域 | 文档数 | 题目数 | 核心考查点 | +| --- | ---: | ---: | --- | +| 保险条款 | 16 | 20 | 保险金计算、退保金额、等待期、免责条款 | +| 监管法规 | 26 | 20 | 法条引用、合规义务、跨文档对比 | +| 金融合同 | 14 | 20 | 债券条款、发行信息、信用评级条件 | +| 财务报表 | 10 | 20 | 年报指标、经营现金流、研发投入 | +| 行业研报 | 20 | 20 | 趋势判断、公司对比、数据核验 | + +--- + +## 第 5 页 + +01题目背景与解析 /1.2评测公式与Token策略 +05 +AFACEO 6 +金融智能创新大赛 +评测公式与 Token 投入策略 +评分公式 +FinalScore = 100 x Acc x (0.7+ 0.3x TokenScore) +TokenScore = max(0,(500万- T_used)/500万)线性递减 +5 Datawhale +TokenScore 与 Token 消耗的线性关系 +Tokenscore 与 Token 消耗的关系 +彎灤 +核心结论 +| TokenScore是线性的,不存在拐点 +每省1个token 的边际收益完全一样,不必焦虑 token 效率 +|花token 答对题,永远是赚的 +即使在90% 准确率场景,答对一题上限仍有13万 token +1 Token 耗尽后可 all-in 准确率 +TokenScore 归零后不再降,所有提准确率操作都值得做 +1.0 +0.8 +0.6 +0.2- +0.0- +160 +答对一题的Token 上限估算 +当前状态 +刚起步 +中等水平 +接近极限 +200 Token 淌耗(万30 +TokenScore = 50050gvs0d +(单: toren) +Tokengcore =。 +400 +500 +Acc +20% +50% +90% +TS +0.9 +0.7 +0.1 +单题上限 +77万 +30万 +13万 +决策原则:准确率从17% 80% 贡献63分;TokenScore 从 0.28 -0.93仅贡献约20%。把精力花在准确率上,ROI 高得多。 + + +### 图表重绘：评分公式与 Token 预算 + +```text +FinalScore = 100 × Acc × (0.7 + 0.3 × TokenScore) +TokenScore = max(0, (500 万 - T_used) / 500 万) +``` + +| 当前状态 | Acc | TokenScore | 单题 Token 上限 | +| --- | ---: | ---: | ---: | +| 刚起步 | 20% | 0.9 | 77 万 | +| 中等水平 | 50% | 0.7 | 30 万 | +| 接近极限 | 90% | 0.1 | 13 万 | + +--- + +## 第 6 页 + +01题目背景与解析/1.3 输入输出格式 +06 +题目输入格式与字段说明 +JSON 输入示例 +Datawhale +AFACEO 6 +金融智能创新大赛 +"'qid": "fin_a_001", +"domain": "financial_reports", +"'question"."研发投入占营收比例?”, +"options": {"A": "5.2%", "B": "8.7%"), +"'answer_format":"mcg", +"type":"事实查找", +"doc_ids":["doc_03", "doc_07"] +关键字段说明 +字段 +qid +domain +answer_format +type +doc_ids +options +类型 +String +String +String +String +Array +Object +说明 +唯一编号:{domain}_{split}_{序号} +五大领域之一 +mcq(单选)/multi(多选)/tf(判断) +题目能力标签(因领域质量差异大) +参考文档编号,A榜提供B榜不提供 +四个选项,键为A/B/C/D +输出格式:answers.csv +summary,5000000,1234567,0 +fin_a_001,A,900,2,902 +fin_a_002,BC,850,3,853 +.首行为汇总:summary.预算,已用,未用 +- 单选:单个字母(A);多选:字母排序连写(BC) +- 判断:T或F;token 统计覆盖所有模型接口调用 +A榜vsB榜关键差异:A榜提供 doc_ids(已知答案所在文档,可离线深度预处理)一B榜不提供doc_ids(需自行检索定位候选文档,难度更高) + + +### 图表重绘：输入输出字段 + +| 字段 | 类型 | 说明 | +| --- | --- | --- | +| `qid` | String | 唯一编号：`{domain}_{split}_{序号}` | +| `domain` | String | 五大领域之一 | +| `answer_format` | String | `mcq`（单选）/ `multi`（多选）/ `tf`（判断） | +| `type` | String | 题目能力标签 | +| `doc_ids` | Array | 参考文档编号；A 榜提供，B 榜不提供 | +| `options` | Object | 四个选项，键为 A/B/C/D | + +--- + +## 第 7 页 + +01题目背景与解析 /1.4 Baseline 整体流程 +Baseline 整体流程 +端到端 Baseline 方案流程图 +加载数据 +题目+文档 Markdown +Datawhale +构建 Prompt +单篇截断≤4000字符 +总上限320K +LLM 调用 +qwen-plus +thinking=False +标准化 +提取字母 +格式校验 +mcq/multi/tf +失败 +正则提取 +A-D +仍无效 +通过 +results_a.json +答案+Token统计 +关键参数与降级策略 +模型配置:qwen-plus,thinking=False +截断策略:单篇截断 ≤4000字符,总上限 80K +降级机制:格式校验失败一正则提取 A-D 一仍无效则默认选A +输出格式:results_a.json(答案+Token 统计)一submission_a.csv +Baseline 定位:先跑通、再优化的最小可行方案 +.无复杂检索:直接加载题目+文档 Markdown +- 无分块处理:硬截断保证上下文完整性 +- 无题型路由:所有题目统一走同一管线 +Baseline 的意义:建立可运行的最小闭环,在此基础上叠加检索优化、题型路由、记忆压缩等策略,逐步逼近上限。 +07 +AFACEO 6 +金融智能创新大赛 +默认A +submission a.csv +赛题提交格式 +7 + + +### 图表重绘：Baseline 整体流程 + +```mermaid +flowchart LR + A["加载数据
题目 + 文档 Markdown"] --> B["构建 Prompt
单篇截断 ≤ 4000 字符
总上限 80K"] + B --> C["LLM 调用
qwen-plus
thinking=False"] + C --> D["标准化
提取答案字母"] + D --> E{"格式校验
mcq / multi / tf"} + E -->|通过| F["results_a.json
答案 + Token 统计"] + E -->|失败| G["正则提取 A-D"] + G -->|仍无效| H["默认答案 A"] + G -->|提取成功| F + H --> F + F --> I["submission_a.csv
赛题提交格式"] +``` + +#### 按时间顺序表达 + +```mermaid +sequenceDiagram + actor Q as 题目 + participant D as 文档 Markdown + participant P as Prompt 构建 + participant L as Qwen-plus + participant V as 输出校验 + participant O as 结果文件 + Q->>D: 读取题目与指定文档 + D->>P: 截断并拼接上下文 + P->>L: 单轮调用（thinking=False） + L-->>V: 返回原始答案 + V->>V: 提取并校验答案字母 + alt 校验通过 + V->>O: 写入 results_a.json + else 校验失败 + V->>V: 正则提取 A-D + alt 仍无有效答案 + V->>O: 默认选择 A + else 提取成功 + V->>O: 写入规范化答案 + end + end + O->>O: 生成 submission_a.csv +``` + +--- + +## 第 8 页 + +01题目背景与解析 /1.5 Baseline Token 消耗分析 +08 +Baseline Token 消耗分析 +核心数据概览(100题A榜) +总 Token 消耗 +376,836 +占500万预算的7.5% +Prompt Token 分布特征 +统计项 +最小 Prompt +中位数 +最大 Prompt +超过 4K 的题数 +数值 +211 +1,952 +7,088 +23 /100 +Datawhale C +)AFACEO 6 +金融智能创新大赛 +平均每题消耗 +3,768 +Prompt 2,403 + Completion 1,365 +剩余预算 +462万+ +还有 92.5%的预算空间可用 +说明 +短文档/简单题,几乎无上下文 +一半题目 prompt 不到2000 +多文档题叠加导致 +仅少数多文档题触发截断 +关键洞察 +1.Token 使用非常克制 +48 题集中在1000-2000 token 区间,多数文档不长,4000 截断很少触发 +2. 预算利用率极低 +只用了 7.5%,大量空间可加检索、多轮推理、自检等策略 +3. 多文档题是消耗大头 +仅23题超过 4Ktoken,这些是需要重点优化的场景 +结论:Baseline 的token 消耗极其保守,准确率提升的空间远大于token 预算的约束。放心加策略。 +8 + +--- + +## 第 9 页 + +## Chapter Two +数据理解与预处理 +Data Understanding and Preprocessing +### 2.1 五大金融文档领域与题型特征 +2.2 +2.3 +PDF 解析方案与检索策略 +数据预处理管线 +02 +6 + +--- + +## 第 10 页 + +02数据理解与预处理/2.1 五大金融文档领域 +五大金融文档领域与题型特征 +领域 +文档 +保险条款 +16 +监管法规 +26 +金融合同 +14 +财务报表 +10 +行业研报 +20 +五种题目能力标签 +国公式计算 +提取公式代入参数计算 +题目 +20 +20 +20 +20 +20 +文档特征 +条款密集、数字精确、结构规整 +法条编号、跨文档引用、表述严谨 +结构化条款、财务术语密集 +表格密集、数字精确、年度对比 +长篇论述、趋势分析、观点混合 +G9跨文档对比 +对比多份文档同类数据 +考查要点 +保险金计算、退保金额、等待期、免责条款 +法条引用、合规义务、跨文档对比 +债券条款、发行信息、信用评级条件 +年报指标、经营现金流、研发投入 +趋势判断、公司对比、数据核验 +联合推理 +多文档信息拼合综合判断 +Datawhale +领域分布:文档数 vs 题目数 +30- +20 - +10- +0- +16 +保险条款 +条款定位 +精确找到特定章节规定 +26 +20 +监管法规 +五大金融文档领域分布 +20 +08 +AFACEOe6 +金融智能创新大赛 +文档数 +题目数 +20 +20 +金融合同 +10 +财务报表 +Q事实查找 +定位带限定词的具体数字 +行业研报 +关键判断:每种类型对检索精度和上下文结构的要求不同,不能使用同一套提示词模板和检索策略处理所有题目。需按题型路由差异化处理。 + +--- + +## 第 11 页 + +02 数据理解与预处理/2.2 PDF 解析与检索策路 +09 +PDF 解析方案与检索策略 +PDF解析方案对比 +方案 +PyMuPDF +Datawhale +AFACEO 6 +金融智能创新大赛 +MinerU +优势 +纯CPU、毫秒级/页、速度快 +VLM+OCR 双模型、扫描件友好 +局限 +无OCR、表格还原度一般 +需GPU、架构复杂 +适用文档 +保险条款、监管法规(文本为主) +财务报表、金融合同(表格密集) +检索策略(无 Embedding/Rerank) +A正刻匹配 +精确匹配数字、百分比、日期、条款编号,零 +token +A BM25 关键词 +词频统计相关性排序,纯CPU,中文分词后稳 +定 +文档结构定位 +利用标题层级、条款编号、页码精确定位 +元数据预筛选 +按领域、文档标识、页码范围过滤 +A榜专属优化 +已知 doc_ids 一离线深度结构化提取一零token 消耗:条款编号映射表/财务指标结构化表/法条引用关系图 +关于分块:agent 自带 grep 工具可直接全文关键词定位。BM25+滑动窗口提取不需分块;保留文档结构时按条款/章节自然边界切分即可,不必纠结块大小。 +8 + +--- + +## 第 12 页 + +02数据理解与预处理/2.3数据预处理管线 +数据预处理管线 +从原始压缩包到结构化 Markdown 的全流程 +- Datawhale +10 +AFACEO 6 +金融智能创新大赛 +public_dataset_a.zip +赛方原始压缩包 +解压ZIP +跳过文件名过长文件 +数据预处理管线 +raw_dataset/ +questions/+raw/ +raw/下各领域 PDF +insurance/regulatory... +pymupdfalm +按页转Markdown +ProcessPool 并行 +关键设计决策 +解析工具:pymupdf4llm,纯CPU,按页转 Markdown +并行处理:ProcessPool 并行加速,多领域同时处理 +输出结构:processed_pymupdf4llm/domain/doc_id/page_NNNN.md +A 榜优势:已知 doc_ids,可离线深度预处理,零 Token 消耗 +预处理的价值: +- 将 PDF 转为结构化 Markdown,保留文本和表格 +. 按页拆分便于后续精准检索和片段提取 +- 离线完成,不消耗500万 Token 预算 +核心原则:预处理是离线投资——花一次时间解析所有文档,答题阶段才能精准检索、高效调用。 +10 + + +### 图表重绘：数据预处理管线 + +```mermaid +flowchart LR + A["public_dataset_a.zip
赛事原始压缩包"] --> B["解压 ZIP
跳过文件名过长文件"] + B --> C["raw_dataset/
questions/ + raw/"] + C --> D["raw/ 各领域 PDF
insurance / regulatory / ..."] + D --> E["pymupdf4llm
按页转 Markdown
ProcessPool 并行"] + E --> F["processed_pymupdf4llm/
domain/doc_id/page_NNNN.md"] +``` + +--- + +## 第 13 页 + +## Chapter Three +题型分类与问答策略 +Question Classification and QA Strategies +3.1 +3.2 +3.3 +题型分类与路由策路 +检索策略与提示词优化 +计算题处理与答案自检 +03 +9 + +--- + +## 第 14 页 + +03 题型分类与问答策略/3.1题型分类与路由 +11 +题型分类与路由策略 +推理类型 +直接事实查找 +条款定位+判断 +公式计算 +跨文档对比 +多文档联合推理 +type 字段质量因领域而异 +领域 +insurance +regulatory +contracts/reports +特征 +答案在文档中明确定义 +答案分散在条款多处 +需提取公式和参数执行计算 +对比多份文档同类数据 +多份文档信息拼合综合判断 +Datawhale C +推荐策略 +精准检索+直接输出,不需要推理链 +多片段检索+逐项验证 +简单运算直接推理;复杂计算(多位数乘除、链式百分比) Python执行 +分文档检索+结构化提取+代码计算 +多文档检索+逐条件验证+逻辑归并 +AFACEO 6 +金融智能创新大赛 +type 实际内容 +事实查询、推理判断、比较分析、计算题 +多选题、判断、mcq、tf(纯冗余) +混合:部分格式+部分领域标签 +可用性 +V可信 +X冗余 +丕需区分 +处理建议 +可直接映射到推理类型分类 +忽略,与 answer_format 完全一致 +结合 answer_format 和题目文本自行判定 +路由原则:有计算的必须走代码,纯判断走大模型;多选题和联合推理出错率更高,优先分配自检 token。 +10 + +--- + +## 第 15 页 + +03题型分类与问答策略/3.2检索与提示词策路 +检索策略与提示词优化 +理想管线:四步流程 +12 +Datawhale +AFACO G +金融智能创新大赛 +离线解析 +结构化预处理 +提示词优化核心技巧 +选项前置 +模型对上下文前部注意力更高,先看到选项再读文档匹配精度更好 +输出极简 +严格约束输出为纯答案字母,省掉推理链 token(复杂题可保留) +系统提示词归零 +能不放系统提示词就不放,每道题重复数百次累积浪费可观 +零 Token 召回 +BM25/正则/结构定位 +极简提示词 +选项前置+片段+问题 +单次模型调用 +输出答案字母 +按题型差异化模板 +多选题:逐项验证模板,对每个选项单独确认文档是否支持 +计算题:提取公式+提取参数模板,数学运算交 Python +判断题:正误对照模板,同时给支持和不支持的证据 +事实查找:精准定位模板,直接给出答案 +A 关键警告:实验表明,粗糙检素(切片破坏连贯性)准确率反而低于硬截断。好的检索需要切分不破坏语义完整性。 +11 + + +### 图表重绘：理想检索管线 + +```mermaid +flowchart LR + A["离线解析
结构化预处理"] --> B["零 Token 召回
BM25 / 正则 / 结构定位"] + B --> C["极简提示词
选项前置 + 片段 + 问题"] + C --> D["单次模型调用
输出答案字母"] +``` + +--- + +## 第 16 页 + +03题型分类与问答策略/3.3计算题与自检 +计算题处理与答案自检 +计算题处理管线 +13 +Datawhale +AFACEO 6 +金融智能创新大赛 +检索文档 +提取公式和参数 +生成代码 +Python 计算脚本 +执行计算 +获取精确结果 +匹配选项 +输出答案字母 +×注意:简单一步运算(加减、小数值乘除)可直接推理;多位数乘除、链式百分比、多步嵌套计算必须走 Python 代码执行 +答案自检策略 +场景一:Token 余粮充足 +每题1000-2000 token,100题只用10-20万 +对多选题、公式计算等高错误率题型做一轮自检 +场景二:Token 接近上限 +不做自检,先保证每道题正常答完 +比多确认一遍更实际 +场景三:Token 已达上限(罚无可罚) +TokenScore 归零且不再降 +放开 all-in:自检、多次采样、换策略重试 +核心原则:计算交给代码,理解和提取交给大模型。自检不是“应不应该”的问题,是"有没有余粮"和”罚无可罚后要不要 all-in"的问题。 +12 + + +### 图表重绘：计算题处理管线 + +```mermaid +flowchart LR + A["检索文档
提取公式和参数"] --> B["生成代码
Python 计算脚本"] + B --> C["执行计算
获得精确结果"] + C --> D["匹配选项
输出答案字母"] +``` + +--- + +## 第 17 页 + +## Chapter Four +Agent上下文管理 +Agent Context Management +### 4.1 从提示词工程到上下文工程 +### 4.2 记忆系统架构与压缩策路 +4.3 +Token 预算与答题策略 +04 +14 + +--- + +## 第 18 页 + +04Agent上下文管理/4.1上下文工程理论框架 +从提示词工程到上下文工程 +15 +Datawhale +范式升级:2023-2024提示词工程(优化单次指令)一2025上下文工程(管理系统性上下文供给)。模型能力越过阈值后,输出不及预期的主因是上下文缺失而非能力不足。 +“上下文窗口有限时,住里面放什么信息、怎么组织,对模型输出的影响超过了提示词本身。“—— Karpathy.2025 +上下文的三类组成 +AFACEOe6 +金融智能创新大赛 +指导性上下文 +信息性上下文 +行动性上下文 +告诉模型做什么、怎么做 +系统提示词、任务描述、少样本示例、输出格式 +一应该极简 +告诉模型需要知道什么 +检索结果、对话历史、长期记忆 +应该精准且精炼 +告诉模型能做什么、做了什么 +工具定义、工具调用历史、返回结果 +一代码执行用于计算 +上下文退化的四种失效模式 +上下文污染 +幻觉进入上下文导致后续推理被污染 +上下文干扰 +无关信息覆盖模型训练知识导致降智 +上下文混淆 +冗余不相关内容让输出偏离期望 +上下文冲突 +上下文中的信息互相矛盾 +四个操作原语 +写入Write +选取 Select +压缩 Compress +隔离 Isolate +14 + + +### 图表重绘：上下文三类组成 + +| 上下文类型 | 作用 | 典型内容 | 优化方向 | +| --- | --- | --- | --- | +| 指导性上下文 | 告诉模型做什么、怎么做 | 系统提示词、任务描述、少样本示例、输出格式 | 应极简 | +| 信息性上下文 | 告诉模型需要知道什么 | 检索结果、对话历史、长期记忆 | 应精准且精炼 | +| 行动性上下文 | 告诉模型能做什么、做过什么 | 工具定义、调用历史、返回结果 | 代码执行用于计算 | + +--- + +## 第 19 页 + +04Agent上下文管理/4.2记忆系统与压缩策路 +记忆系统架构与压缩策略 +三层记忆架构+结构化工作记忆(按文档分节维护,每题增量更新) +工作记忆 ~内存(RAM) +当前上下文窗口中的内容 +会话级易失,窗口管表达 +16 +Datawhale +AFACEO G +金融智能创新大赛 +短期记忆 ~磁盘缓存 +当前会话的压缩历史 +会话内持久,增量写入 +长期记忆 ~硬盘归档 +跨会话的知识、事实和偏好 +持久存储,结构化索引 +“丢失在中间“效应 (Lost in the Middle) +- 大模型对上下文的理解呈 U 型曲线:开头和结尾最好,中间显著衰减 +- 模型越大,U型越明显;即使无干扰信息,token增加推理质量也下降 +- "100 万上下文窗口"更多是检索能力,不是全量理解能力 +四层过滤管线 +元数据预过滤 +按领域/文档标识筛选 +关键词粗筛(BM25) +精确匹配数字/术语 +实践建议 +- 最重要信息放开头和结尾 +- 一个窗口不超过5个片段 +- 3个精准片段>10 个粗糙片段 +- 检索为主、压缩为辅 +文档结构定位 +标题层级/条款编号跳转 +送入大模型 +仅3个精锐片段 +生产级压缩体系(五层递进,能不用大模型就不调用) +清除过期缓存 +剥离旧消息 +空闲60分钟自动清理 +保留细粒度上下文 +多Agent隔离:子Agent独立处理各自文档,只向主控返回结构化摘要,避免信息混淆 +读时投影 +移除已归档消息 +结构化记忆替代 +零模型调用 +大模型全量压缩 +回退路径 +15 + + +### 图表重绘：记忆架构与压缩管线 + +```mermaid +flowchart LR + W["工作记忆 ≈ 内存 RAM
当前上下文窗口"] --> S["短期记忆 ≈ 磁盘缓存
当前会话的压缩历史"] + S --> L["长期记忆 ≈ 硬盘归档
跨会话知识、事实和偏好"] +``` + +```mermaid +flowchart LR + A["元数据预过滤
按领域 / 文档标识筛选"] --> B["关键词粗筛
BM25 精确匹配数字和术语"] + B --> C["文档结构定位
标题层级 / 条款编号跳转"] + C --> D["送入大模型
仅 3 个精锐片段"] +``` + +```mermaid +flowchart LR + A["清除过期缓存
空闲 60 分钟自动清理"] --> B["剥离旧消息
保留细粒度上下文"] + B --> C["读时投影
移除已归档消息"] + C --> D["结构化记忆替代
零模型调用"] + D --> E["大模型全量压缩
回退路径"] +``` + +--- + +## 第 20 页 + +04Agent上下文管理/4.3 Token 预算与答题策略 +Token 预算分配与答题策略设计 +最优估算:单题Token 消耗 +-1,000 +单题token(检索3片段) +-10万 +A榜100题总消耗 +答题路由设计 +题型 +事实查找/条款定位 +公式计算 +跨文档丿联合推理 +独立回答 vs连续推理复用 +独立回答(推荐) +每题从零开始,通过结构化工作记忆跨题复用已验证信息 +管线 +单次问答管线 +代码执行管线 +Agent管线 +17 +Datawhale +AFACEO 6 +金融智能创新大赛 +3.5×+ +相比硬截断的优化空间 +-100万 +复杂题多步推理上限 +400万+ +可大胆投入的余量 +策略 +BM25 检索 —极简提示词输出答案 +检索提取 Python 计算>匹配选项 +多步检索逐条件验证逻辑归并 +子 Agent 隔离回答 +跨文档对比题各子 agent处理一份文档,主控只接收精炼结果 +16 + + +### 图表重绘：题型路由策略 + +| 题型 | 管线 | 策略 | +| --- | --- | --- | +| 事实查找 / 条款定位 | 单次问答管线 | BM25 检索 → 极简提示词 → 输出答案 | +| 公式计算 | 代码执行管线 | 检索提取 → Python 计算 → 匹配选项 | +| 跨文档 / 联合推理 | Agent 管线 | 多步检索 → 逐条件验证 → 逻辑归并 | + +--- + +## 第 21 页 + +05 总结与核心判断 +总结与核心判断 +18 +AFACEO 6 +金融智能创新大赛 +1 +上下文工程优先于提示词工程 +构建动态上下文供给系统,让模型每次只看到最需要的信息 +4 +2 +检索是最好的压缩 +把几十页文档浓缩成几个精准片段,且不消耗token +5 +Datawhale +TokenScore 是线性的,不要焦虑 +把token 花在能提升准确率的地方,哪怕多花一些也是对的决策 +上下文窗口是内存,记忆系统是硬盘 +只在需要时加载相关信息到工作记忆,不试图塞满所有内容 +3 +不要让大模型做不擅长的事 +数学计算交给代码,精确匹配交给关键词,语义理解才交给模型 +6 +写入/选取/ 压缩/隔离 +四个操作原语是上下文工程的通用语言,适用于竞赛到生产全场景 +真正拉开差距的永远是准确率。TokenScore线性无拐点,把token花在能提升准确率的地方,哪怕多花也是对的决策。 +"多数agent 的失败,根源在上下文工程的失败,而非模型能力的不足。” +-- Karpathy, 2025 +17 + +--- + +## 第 22 页 + +拿下赛题四 +上下文工程,是 Agent 系统的核心战场 +扫码加入赛题交流群,获取更多实战干货 +AFAC +钉钉赛题交流群 +AFAC公众号 +AFAC 大赛小助手 +Datawhale +& +A +人FAC口G +金融智能创新大赛 +18 + +--- diff --git "a/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224-\347\262\276\347\256\200\347\211\210.md" "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224-\347\262\276\347\256\200\347\211\210.md" new file mode 100644 index 0000000..714b7b4 --- /dev/null +++ "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224-\347\262\276\347\256\200\347\211\210.md" @@ -0,0 +1,736 @@ +# 金融长文本 Agent 的动态记忆压缩与高效问答 + +> 本版仅保留与赛题直接相关的正文、公式、表格、流程图和策略；封面、目录、品牌宣传、二维码和 Q&A 页面已省略。 +> 图表重绘部分为基于原 PDF 的 Mermaid/Markdown 语义版，原始视觉效果仍以 PDF 为准。 + +## PDF 第 4 页 + +赛事概览与核心约束 +赛题核心设定 +- 数据:200题,五大金融领域 +.基座模型:仅限 Qwen 系列,禁止微调/reasoning +.禁止 Embedding 和 Rerank 模型(经赛题方确认) +- Token 预算:全部接口调用总和≤500 万 +- 评测:准确率70%+Token 效率30% +核心挑战 +A榜vS B榜差异 +A 榜提供 doc_ids,B榜需自行检索定位 + +关键数据 +200 +总题目数 +500万 +Token 预算上限 +无向量检索 +Embedding/Rerank 不可用,退回规则方法 +五类文档差异巨大 +保险/法规/合同/报表/研报,结构各异 +核心洞察:多数agent 的失败,根源在上下文工程的失败,而非模型能力的不足。给定固定模型和token 预算,怎么让模型读最少但最对的信息,是关键问题。 +领域 +保险条款 +监管法规 +金融合同 +财务报表 +行业研报 +文档数 +16 +26 +14 +10 +20 +题目数 +20 +20 +20 +20 +20 +核心考查点 +保险金计算、退保金额、等待期、免责条款 +法条引用、合规义务、跨文档对比 +债券条款、发行信息、信用评级条件 +年报指标、经营现金流、研发投入 +趋势判断、公司对比、数据核验 + +### 图表重绘：五大领域分布 + +| 领域 | 文档数 | 题目数 | 核心考查点 | +| --- | ---: | ---: | --- | +| 保险条款 | 16 | 20 | 保险金计算、退保金额、等待期、免责条款 | +| 监管法规 | 26 | 20 | 法条引用、合规义务、跨文档对比 | +| 金融合同 | 14 | 20 | 债券条款、发行信息、信用评级条件 | +| 财务报表 | 10 | 20 | 年报指标、经营现金流、研发投入 | +| 行业研报 | 20 | 20 | 趋势判断、公司对比、数据核验 | + +--- + +--- + +## PDF 第 5 页 + +评测公式与 Token 投入策略 +评分公式 +FinalScore = 100 x Acc x (0.7+ 0.3x TokenScore) +TokenScore = max(0,(500万- T_used)/500万)线性递减 + +核心结论 +| TokenScore是线性的,不存在拐点 +每省1个token 的边际收益完全一样,不必焦虑 token 效率 +|花token 答对题,永远是赚的 +即使在90% 准确率场景,答对一题上限仍有13万 token +1 Token 耗尽后可 all-in 准确率 +TokenScore 归零后不再降,所有提准确率操作都值得做 + +决策原则:准确率从17% 80% 贡献63分;TokenScore 从 0.28 -0.93仅贡献约20%。把精力花在准确率上,ROI 高得多。 + +### 图表重绘：评分公式与 Token 预算 + +```text +FinalScore = 100 × Acc × (0.7 + 0.3 × TokenScore) +TokenScore = max(0, (500 万 - T_used) / 500 万) +``` + +| 当前状态 | Acc | TokenScore | 单题 Token 上限 | +| --- | ---: | ---: | ---: | +| 刚起步 | 20% | 0.9 | 77 万 | +| 中等水平 | 50% | 0.7 | 30 万 | +| 接近极限 | 90% | 0.1 | 13 万 | + +--- + +--- + +## PDF 第 6 页 + +题目输入格式与字段说明 +JSON 输入示例 + +"'qid": "fin_a_001", +"domain": "financial_reports", +"'question"."研发投入占营收比例?”, +"options": {"A": "5.2%", "B": "8.7%"), +"'answer_format":"mcg", +"type":"事实查找", +"doc_ids":["doc_03", "doc_07"] +关键字段说明 +字段 +qid +domain +answer_format +type +doc_ids +options +类型 +String +String +String +String +Array +Object +说明 +唯一编号:{domain}_{split}_{序号} +五大领域之一 +mcq(单选)/multi(多选)/tf(判断) +题目能力标签(因领域质量差异大) +参考文档编号,A榜提供B榜不提供 +四个选项,键为A/B/C/D +输出格式:answers.csv +summary,5000000,1234567,0 +fin_a_001,A,900,2,902 +fin_a_002,BC,850,3,853 +.首行为汇总:summary.预算,已用,未用 +- 单选:单个字母(A);多选:字母排序连写(BC) +- 判断:T或F;token 统计覆盖所有模型接口调用 +A榜vsB榜关键差异:A榜提供 doc_ids(已知答案所在文档,可离线深度预处理)一B榜不提供doc_ids(需自行检索定位候选文档,难度更高) + +### 图表重绘：输入输出字段 + +| 字段 | 类型 | 说明 | +| --- | --- | --- | +| `qid` | String | 唯一编号：`{domain}_{split}_{序号}` | +| `domain` | String | 五大领域之一 | +| `answer_format` | String | `mcq`（单选）/ `multi`（多选）/ `tf`（判断） | +| `type` | String | 题目能力标签 | +| `doc_ids` | Array | 参考文档编号；A 榜提供，B 榜不提供 | +| `options` | Object | 四个选项，键为 A/B/C/D | + +--- + +--- + +## PDF 第 7 页 + +Baseline 整体流程 +端到端 Baseline 方案流程图 +加载数据 +题目+文档 Markdown + +构建 Prompt +单篇截断≤4000字符 +总上限320K +LLM 调用 +qwen-plus +thinking=False +标准化 +提取字母 +格式校验 +mcq/multi/tf +失败 +正则提取 +A-D +仍无效 +通过 +results_a.json +答案+Token统计 +关键参数与降级策略 +模型配置:qwen-plus,thinking=False +截断策略:单篇截断 ≤4000字符,总上限 80K +降级机制:格式校验失败一正则提取 A-D 一仍无效则默认选A +输出格式:results_a.json(答案+Token 统计)一submission_a.csv +Baseline 定位:先跑通、再优化的最小可行方案 +.无复杂检索:直接加载题目+文档 Markdown +- 无分块处理:硬截断保证上下文完整性 +- 无题型路由:所有题目统一走同一管线 +Baseline 的意义:建立可运行的最小闭环,在此基础上叠加检索优化、题型路由、记忆压缩等策略,逐步逼近上限。 + +默认A +submission a.csv +赛题提交格式 + +### 图表重绘：Baseline 整体流程 + +```mermaid +flowchart LR + A["加载数据
题目 + 文档 Markdown"] --> B["构建 Prompt
单篇截断 ≤ 4000 字符
总上限 80K"] + B --> C["LLM 调用
qwen-plus
thinking=False"] + C --> D["标准化
提取答案字母"] + D --> E{"格式校验
mcq / multi / tf"} + E -->|通过| F["results_a.json
答案 + Token 统计"] + E -->|失败| G["正则提取 A-D"] + G -->|仍无效| H["默认答案 A"] + G -->|提取成功| F + H --> F + F --> I["submission_a.csv
赛题提交格式"] +``` + +#### 按时间顺序表达 + +```mermaid +sequenceDiagram + actor Q as 题目 + participant D as 文档 Markdown + participant P as Prompt 构建 + participant L as Qwen-plus + participant V as 输出校验 + participant O as 结果文件 + Q->>D: 读取题目与指定文档 + D->>P: 截断并拼接上下文 + P->>L: 单轮调用（thinking=False） + L-->>V: 返回原始答案 + V->>V: 提取并校验答案字母 + alt 校验通过 + V->>O: 写入 results_a.json + else 校验失败 + V->>V: 正则提取 A-D + alt 仍无有效答案 + V->>O: 默认选择 A + else 提取成功 + V->>O: 写入规范化答案 + end + end + O->>O: 生成 submission_a.csv +``` + +--- + +--- + +## PDF 第 8 页 + +Baseline Token 消耗分析 +核心数据概览(100题A榜) +总 Token 消耗 +376,836 +占500万预算的7.5% +Prompt Token 分布特征 +统计项 +最小 Prompt +中位数 +最大 Prompt +超过 4K 的题数 +数值 +211 +1,952 +7,088 +23 /100 + +平均每题消耗 +3,768 +Prompt 2,403 + Completion 1,365 +剩余预算 +462万+ +还有 92.5%的预算空间可用 +说明 +短文档/简单题,几乎无上下文 +一半题目 prompt 不到2000 +多文档题叠加导致 +仅少数多文档题触发截断 +关键洞察 +1.Token 使用非常克制 +48 题集中在1000-2000 token 区间,多数文档不长,4000 截断很少触发 +2. 预算利用率极低 +只用了 7.5%,大量空间可加检索、多轮推理、自检等策略 +3. 多文档题是消耗大头 +仅23题超过 4Ktoken,这些是需要重点优化的场景 +结论:Baseline 的token 消耗极其保守,准确率提升的空间远大于token 预算的约束。放心加策略。 + +--- + +--- + +## PDF 第 10 页 + +五大金融文档领域与题型特征 +领域 +文档 +保险条款 + +监管法规 +26 +金融合同 +14 +财务报表 + +行业研报 +20 +五种题目能力标签 +国公式计算 +提取公式代入参数计算 +题目 +20 +20 +20 +20 +20 +文档特征 +条款密集、数字精确、结构规整 +法条编号、跨文档引用、表述严谨 +结构化条款、财务术语密集 +表格密集、数字精确、年度对比 +长篇论述、趋势分析、观点混合 + +对比多份文档同类数据 +考查要点 +保险金计算、退保金额、等待期、免责条款 +法条引用、合规义务、跨文档对比 +债券条款、发行信息、信用评级条件 +年报指标、经营现金流、研发投入 +趋势判断、公司对比、数据核验 +联合推理 +多文档信息拼合综合判断 + +关键判断:每种类型对检索精度和上下文结构的要求不同,不能使用同一套提示词模板和检索策略处理所有题目。需按题型路由差异化处理。 + +--- + +--- + +## PDF 第 11 页 + +PDF 解析方案与检索策略 +PDF解析方案对比 +方案 +PyMuPDF + +MinerU +优势 +纯CPU、毫秒级/页、速度快 +VLM+OCR 双模型、扫描件友好 +局限 +无OCR、表格还原度一般 +需GPU、架构复杂 +适用文档 +保险条款、监管法规(文本为主) +财务报表、金融合同(表格密集) +检索策略(无 Embedding/Rerank) +A正刻匹配 +精确匹配数字、百分比、日期、条款编号,零 +token +A BM25 关键词 +词频统计相关性排序,纯CPU,中文分词后稳 +定 +文档结构定位 +利用标题层级、条款编号、页码精确定位 +元数据预筛选 +按领域、文档标识、页码范围过滤 +A榜专属优化 +已知 doc_ids 一离线深度结构化提取一零token 消耗:条款编号映射表/财务指标结构化表/法条引用关系图 +关于分块:agent 自带 grep 工具可直接全文关键词定位。BM25+滑动窗口提取不需分块;保留文档结构时按条款/章节自然边界切分即可,不必纠结块大小。 + +--- + +--- + +## PDF 第 12 页 + +数据预处理管线 +从原始压缩包到结构化 Markdown 的全流程 + +public_dataset_a.zip +赛方原始压缩包 +解压ZIP +跳过文件名过长文件 +数据预处理管线 +raw_dataset/ +questions/+raw/ +raw/下各领域 PDF +insurance/regulatory... +pymupdfalm +按页转Markdown +ProcessPool 并行 +关键设计决策 +解析工具:pymupdf4llm,纯CPU,按页转 Markdown +并行处理:ProcessPool 并行加速,多领域同时处理 +输出结构:processed_pymupdf4llm/domain/doc_id/page_NNNN.md +A 榜优势:已知 doc_ids,可离线深度预处理,零 Token 消耗 +预处理的价值: +- 将 PDF 转为结构化 Markdown,保留文本和表格 +. 按页拆分便于后续精准检索和片段提取 +- 离线完成,不消耗500万 Token 预算 +核心原则:预处理是离线投资——花一次时间解析所有文档,答题阶段才能精准检索、高效调用。 + +### 图表重绘：数据预处理管线 + +```mermaid +flowchart LR + A["public_dataset_a.zip
赛事原始压缩包"] --> B["解压 ZIP
跳过文件名过长文件"] + B --> C["raw_dataset/
questions/ + raw/"] + C --> D["raw/ 各领域 PDF
insurance / regulatory / ..."] + D --> E["pymupdf4llm
按页转 Markdown
ProcessPool 并行"] + E --> F["processed_pymupdf4llm/
domain/doc_id/page_NNNN.md"] +``` + +--- + +--- + +## PDF 第 14 页 + +03 题型分类与问答策略/3.1题型分类与路由 + +题型分类与路由策略 +推理类型 +直接事实查找 +条款定位+判断 +公式计算 +跨文档对比 +多文档联合推理 +type 字段质量因领域而异 +领域 +insurance +regulatory +contracts/reports +特征 +答案在文档中明确定义 +答案分散在条款多处 +需提取公式和参数执行计算 +对比多份文档同类数据 +多份文档信息拼合综合判断 + +推荐策略 +精准检索+直接输出,不需要推理链 +多片段检索+逐项验证 +简单运算直接推理;复杂计算(多位数乘除、链式百分比) Python执行 +分文档检索+结构化提取+代码计算 +多文档检索+逐条件验证+逻辑归并 + +type 实际内容 +事实查询、推理判断、比较分析、计算题 +多选题、判断、mcq、tf(纯冗余) +混合:部分格式+部分领域标签 +可用性 +V可信 +X冗余 +丕需区分 +处理建议 +可直接映射到推理类型分类 +忽略,与 answer_format 完全一致 +结合 answer_format 和题目文本自行判定 +路由原则:有计算的必须走代码,纯判断走大模型;多选题和联合推理出错率更高,优先分配自检 token。 + +--- + +--- + +## PDF 第 15 页 + +检索策略与提示词优化 +理想管线:四步流程 + +离线解析 +结构化预处理 +提示词优化核心技巧 +选项前置 +模型对上下文前部注意力更高,先看到选项再读文档匹配精度更好 +输出极简 +严格约束输出为纯答案字母,省掉推理链 token(复杂题可保留) +系统提示词归零 +能不放系统提示词就不放,每道题重复数百次累积浪费可观 +零 Token 召回 +BM25/正则/结构定位 +极简提示词 +选项前置+片段+问题 +单次模型调用 +输出答案字母 +按题型差异化模板 +多选题:逐项验证模板,对每个选项单独确认文档是否支持 +计算题:提取公式+提取参数模板,数学运算交 Python +判断题:正误对照模板,同时给支持和不支持的证据 +事实查找:精准定位模板,直接给出答案 +A 关键警告:实验表明,粗糙检素(切片破坏连贯性)准确率反而低于硬截断。好的检索需要切分不破坏语义完整性。 + +### 图表重绘：理想检索管线 + +```mermaid +flowchart LR + A["离线解析
结构化预处理"] --> B["零 Token 召回
BM25 / 正则 / 结构定位"] + B --> C["极简提示词
选项前置 + 片段 + 问题"] + C --> D["单次模型调用
输出答案字母"] +``` + +--- + +--- + +## PDF 第 16 页 + +计算题处理与答案自检 +计算题处理管线 + +检索文档 +提取公式和参数 +生成代码 +Python 计算脚本 +执行计算 +获取精确结果 +匹配选项 +输出答案字母 +×注意:简单一步运算(加减、小数值乘除)可直接推理;多位数乘除、链式百分比、多步嵌套计算必须走 Python 代码执行 +答案自检策略 +场景一:Token 余粮充足 +每题1000-2000 token,100题只用10-20万 +对多选题、公式计算等高错误率题型做一轮自检 +场景二:Token 接近上限 +不做自检,先保证每道题正常答完 +比多确认一遍更实际 +场景三:Token 已达上限(罚无可罚) +TokenScore 归零且不再降 +放开 all-in:自检、多次采样、换策略重试 +核心原则:计算交给代码,理解和提取交给大模型。自检不是“应不应该”的问题,是"有没有余粮"和”罚无可罚后要不要 all-in"的问题。 + +### 图表重绘：计算题处理管线 + +```mermaid +flowchart LR + A["检索文档
提取公式和参数"] --> B["生成代码
Python 计算脚本"] + B --> C["执行计算
获得精确结果"] + C --> D["匹配选项
输出答案字母"] +``` + +--- + +--- + +## PDF 第 18 页 + +从提示词工程到上下文工程 + +范式升级:2023-2024提示词工程(优化单次指令)一2025上下文工程(管理系统性上下文供给)。模型能力越过阈值后,输出不及预期的主因是上下文缺失而非能力不足。 +“上下文窗口有限时,住里面放什么信息、怎么组织,对模型输出的影响超过了提示词本身。“—— Karpathy.2025 +上下文的三类组成 + +指导性上下文 +信息性上下文 +行动性上下文 +告诉模型做什么、怎么做 +系统提示词、任务描述、少样本示例、输出格式 +一应该极简 +告诉模型需要知道什么 +检索结果、对话历史、长期记忆 +应该精准且精炼 +告诉模型能做什么、做了什么 +工具定义、工具调用历史、返回结果 +一代码执行用于计算 +上下文退化的四种失效模式 +上下文污染 +幻觉进入上下文导致后续推理被污染 +上下文干扰 +无关信息覆盖模型训练知识导致降智 +上下文混淆 +冗余不相关内容让输出偏离期望 +上下文冲突 +上下文中的信息互相矛盾 +四个操作原语 +写入Write +选取 Select +压缩 Compress +隔离 Isolate + +### 图表重绘：上下文三类组成 + +| 上下文类型 | 作用 | 典型内容 | 优化方向 | +| --- | --- | --- | --- | +| 指导性上下文 | 告诉模型做什么、怎么做 | 系统提示词、任务描述、少样本示例、输出格式 | 应极简 | +| 信息性上下文 | 告诉模型需要知道什么 | 检索结果、对话历史、长期记忆 | 应精准且精炼 | +| 行动性上下文 | 告诉模型能做什么、做过什么 | 工具定义、调用历史、返回结果 | 代码执行用于计算 | + +--- + +--- + +## PDF 第 19 页 + +记忆系统架构与压缩策略 +三层记忆架构+结构化工作记忆(按文档分节维护,每题增量更新) +工作记忆 ~内存(RAM) +当前上下文窗口中的内容 +会话级易失,窗口管表达 + +短期记忆 ~磁盘缓存 +当前会话的压缩历史 +会话内持久,增量写入 +长期记忆 ~硬盘归档 +跨会话的知识、事实和偏好 +持久存储,结构化索引 +“丢失在中间“效应 (Lost in the Middle) +- 大模型对上下文的理解呈 U 型曲线:开头和结尾最好,中间显著衰减 +- 模型越大,U型越明显;即使无干扰信息,token增加推理质量也下降 +- "100 万上下文窗口"更多是检索能力,不是全量理解能力 +四层过滤管线 +元数据预过滤 +按领域/文档标识筛选 +关键词粗筛(BM25) +精确匹配数字/术语 +实践建议 +- 最重要信息放开头和结尾 +- 一个窗口不超过5个片段 +- 3个精准片段>10 个粗糙片段 +- 检索为主、压缩为辅 +文档结构定位 +标题层级/条款编号跳转 +送入大模型 +仅3个精锐片段 +生产级压缩体系(五层递进,能不用大模型就不调用) +清除过期缓存 +剥离旧消息 +空闲60分钟自动清理 +保留细粒度上下文 +多Agent隔离:子Agent独立处理各自文档,只向主控返回结构化摘要,避免信息混淆 +读时投影 +移除已归档消息 +结构化记忆替代 +零模型调用 +大模型全量压缩 +回退路径 + +### 图表重绘：记忆架构与压缩管线 + +```mermaid +flowchart LR + W["工作记忆 ≈ 内存 RAM
当前上下文窗口"] --> S["短期记忆 ≈ 磁盘缓存
当前会话的压缩历史"] + S --> L["长期记忆 ≈ 硬盘归档
跨会话知识、事实和偏好"] +``` + +```mermaid +flowchart LR + A["元数据预过滤
按领域 / 文档标识筛选"] --> B["关键词粗筛
BM25 精确匹配数字和术语"] + B --> C["文档结构定位
标题层级 / 条款编号跳转"] + C --> D["送入大模型
仅 3 个精锐片段"] +``` + +```mermaid +flowchart LR + A["清除过期缓存
空闲 60 分钟自动清理"] --> B["剥离旧消息
保留细粒度上下文"] + B --> C["读时投影
移除已归档消息"] + C --> D["结构化记忆替代
零模型调用"] + D --> E["大模型全量压缩
回退路径"] +``` + +--- + +--- + +## PDF 第 20 页 + +Token 预算分配与答题策略设计 +最优估算:单题Token 消耗 +-1,000 +单题token(检索3片段) +-10万 +A榜100题总消耗 +答题路由设计 +题型 +事实查找/条款定位 +公式计算 +跨文档丿联合推理 +独立回答 vs连续推理复用 +独立回答(推荐) +每题从零开始,通过结构化工作记忆跨题复用已验证信息 +管线 +单次问答管线 +代码执行管线 +Agent管线 + +3.5×+ +相比硬截断的优化空间 +-100万 +复杂题多步推理上限 +400万+ +可大胆投入的余量 +策略 +BM25 检索 —极简提示词输出答案 +检索提取 Python 计算>匹配选项 +多步检索逐条件验证逻辑归并 +子 Agent 隔离回答 +跨文档对比题各子 agent处理一份文档,主控只接收精炼结果 + +### 图表重绘：题型路由策略 + +| 题型 | 管线 | 策略 | +| --- | --- | --- | +| 事实查找 / 条款定位 | 单次问答管线 | BM25 检索 → 极简提示词 → 输出答案 | +| 公式计算 | 代码执行管线 | 检索提取 → Python 计算 → 匹配选项 | +| 跨文档 / 联合推理 | Agent 管线 | 多步检索 → 逐条件验证 → 逻辑归并 | + +--- + +--- + +## PDF 第 21 页 + +总结与核心判断 + +上下文工程优先于提示词工程 +构建动态上下文供给系统,让模型每次只看到最需要的信息 +4 +2 +检索是最好的压缩 +把几十页文档浓缩成几个精准片段,且不消耗token +5 + +TokenScore 是线性的,不要焦虑 +把token 花在能提升准确率的地方,哪怕多花一些也是对的决策 +上下文窗口是内存,记忆系统是硬盘 +只在需要时加载相关信息到工作记忆,不试图塞满所有内容 +3 +不要让大模型做不擅长的事 +数学计算交给代码,精确匹配交给关键词,语义理解才交给模型 +6 +写入/选取/ 压缩/隔离 +四个操作原语是上下文工程的通用语言,适用于竞赛到生产全场景 +真正拉开差距的永远是准确率。TokenScore线性无拐点,把token花在能提升准确率的地方,哪怕多花也是对的决策。 +"多数agent 的失败,根源在上下文工程的失败,而非模型能力的不足。” +-- Karpathy, 2025 + +--- + +--- diff --git "a/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224.md" "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224.md" new file mode 100644 index 0000000..5042135 --- /dev/null +++ "b/output/pdf/AFAC-\350\265\233\351\242\230\345\233\233-Markdown/\351\207\221\350\236\215\351\225\277\346\226\207\346\234\254 Agent \347\232\204\345\212\250\346\200\201\350\256\260\345\277\206\345\216\213\347\274\251\344\270\216\351\253\230\346\225\210\351\227\256\347\255\224.md" @@ -0,0 +1,809 @@ +# 金融长文本 Agent 的动态记忆压缩与高效问答 + +> 来源：`金融长文本 Agent 的动态记忆压缩与高效问答.pdf` +> +> 注：原 PDF 的内嵌字体编码异常，正文采用中文 OCR 提取；图表中的少量字符可能存在识别误差。 + +## 第 1 页 + +AFAC2026 挑战组赛题四 +Financial Long-Text Agent Challenge +TECH SHARE +金融长文本Agent的动态记忆压缩与高效问 +答 +Dynamic Memory Compression & Efficient QA for Financial Long-Text Agent +李秀奇(筱可) +Datawhale 成员 |AFAC2026 赛道 baseline 教程作者 + +--- + +## 第 2 页 + +目录 +TABLE OF CONTENTS +CONTENTS +01 +题目背景与解析 +Background and Problem Analysis +02 +数据理解与预处理 +Data Understanding and Preprocessing +03 +题型分类与问答策略 +Question Classification and QA Strategies +04 +Agent 上下文管理 +Agent Context Management +05 +总结与核心判断 +Summary and Key Insights +- 1.1 赛事概览与核心约束 +- 1.2 评测公式与 Token 策略 +- 1.3 输入输出格式 +- 1.4 Baseline 整体流程 +- 1.5 Baseline Token 消耗分析 +- 2.1 五大金融文档领域与题型特征 +- 2.2 PDF 解析方案与检索策略 +- 2.3 数据预处理管线 +.3.1题型分类与路由策略 +- 3.2 检索策略与提示词优化 +- 3.3计算题处理与答案自检 +- 4.1从提示词工程到上下文工程 +- 4.2 记忆系统架构与压缩策略 +. 4.3 Token 预算与答题策略 +- 5.1总结与核心判断 + +--- + +## 第 3 页 + +## Chapter One +题目背景与解析 +Background and Problem Analysis +1.1 +1.2 +赛事概览与核心约束 +评测公式与 Token 投入策略 +〇1 + +--- + +## 第 4 页 + +01题目背景与解析/1.1赛事概览与核心约束 +04 +赛事概览与核心约束 +赛题核心设定 +- 数据:200题,五大金融领域 +.基座模型:仅限 Qwen 系列,禁止微调/reasoning +.禁止 Embedding 和 Rerank 模型(经赛题方确认) +- Token 预算:全部接口调用总和≤500 万 +- 评测:准确率70%+Token 效率30% +核心挑战 +A榜vS B榜差异 +A 榜提供 doc_ids,B榜需自行检索定位 +- Datawhale +AFACO G +金融智能创新大赛 +关键数据 +200 +总题目数 +500万 +Token 预算上限 +无向量检索 +Embedding/Rerank 不可用,退回规则方法 +五类文档差异巨大 +保险/法规/合同/报表/研报,结构各异 +核心洞察:多数agent 的失败,根源在上下文工程的失败,而非模型能力的不足。给定固定模型和token 预算,怎么让模型读最少但最对的信息,是关键问题。 +领域 +保险条款 +监管法规 +金融合同 +财务报表 +行业研报 +文档数 +16 +26 +14 +10 +20 +题目数 +20 +20 +20 +20 +20 +核心考查点 +保险金计算、退保金额、等待期、免责条款 +法条引用、合规义务、跨文档对比 +债券条款、发行信息、信用评级条件 +年报指标、经营现金流、研发投入 +趋势判断、公司对比、数据核验 + +--- + +## 第 5 页 + +01题目背景与解析 /1.2评测公式与Token策略 +05 +AFACEO 6 +金融智能创新大赛 +评测公式与 Token 投入策略 +评分公式 +FinalScore = 100 x Acc x (0.7+ 0.3x TokenScore) +TokenScore = max(0,(500万- T_used)/500万)线性递减 +5 Datawhale +TokenScore 与 Token 消耗的线性关系 +Tokenscore 与 Token 消耗的关系 +彎灤 +核心结论 +| TokenScore是线性的,不存在拐点 +每省1个token 的边际收益完全一样,不必焦虑 token 效率 +|花token 答对题,永远是赚的 +即使在90% 准确率场景,答对一题上限仍有13万 token +1 Token 耗尽后可 all-in 准确率 +TokenScore 归零后不再降,所有提准确率操作都值得做 +1.0 +0.8 +0.6 +0.2- +0.0- +160 +答对一题的Token 上限估算 +当前状态 +刚起步 +中等水平 +接近极限 +200 Token 淌耗(万30 +TokenScore = 50050gvs0d +(单: toren) +Tokengcore =。 +400 +500 +Acc +20% +50% +90% +TS +0.9 +0.7 +0.1 +单题上限 +77万 +30万 +13万 +决策原则:准确率从17% 80% 贡献63分;TokenScore 从 0.28 -0.93仅贡献约20%。把精力花在准确率上,ROI 高得多。 + +--- + +## 第 6 页 + +01题目背景与解析/1.3 输入输出格式 +06 +题目输入格式与字段说明 +JSON 输入示例 +Datawhale +AFACEO 6 +金融智能创新大赛 +"'qid": "fin_a_001", +"domain": "financial_reports", +"'question"."研发投入占营收比例?”, +"options": {"A": "5.2%", "B": "8.7%"), +"'answer_format":"mcg", +"type":"事实查找", +"doc_ids":["doc_03", "doc_07"] +关键字段说明 +字段 +qid +domain +answer_format +type +doc_ids +options +类型 +String +String +String +String +Array +Object +说明 +唯一编号:{domain}_{split}_{序号} +五大领域之一 +mcq(单选)/multi(多选)/tf(判断) +题目能力标签(因领域质量差异大) +参考文档编号,A榜提供B榜不提供 +四个选项,键为A/B/C/D +输出格式:answers.csv +summary,5000000,1234567,0 +fin_a_001,A,900,2,902 +fin_a_002,BC,850,3,853 +.首行为汇总:summary.预算,已用,未用 +- 单选:单个字母(A);多选:字母排序连写(BC) +- 判断:T或F;token 统计覆盖所有模型接口调用 +A榜vsB榜关键差异:A榜提供 doc_ids(已知答案所在文档,可离线深度预处理)一B榜不提供doc_ids(需自行检索定位候选文档,难度更高) + +--- + +## 第 7 页 + +01题目背景与解析 /1.4 Baseline 整体流程 +Baseline 整体流程 +端到端 Baseline 方案流程图 +加载数据 +题目+文档 Markdown +Datawhale +构建 Prompt +单篇截断≤4000字符 +总上限320K +LLM 调用 +qwen-plus +thinking=False +标准化 +提取字母 +格式校验 +mcq/multi/tf +失败 +正则提取 +A-D +仍无效 +通过 +results_a.json +答案+Token统计 +关键参数与降级策略 +模型配置:qwen-plus,thinking=False +截断策略:单篇截断 ≤4000字符,总上限 80K +降级机制:格式校验失败一正则提取 A-D 一仍无效则默认选A +输出格式:results_a.json(答案+Token 统计)一submission_a.csv +Baseline 定位:先跑通、再优化的最小可行方案 +.无复杂检索:直接加载题目+文档 Markdown +- 无分块处理:硬截断保证上下文完整性 +- 无题型路由:所有题目统一走同一管线 +Baseline 的意义:建立可运行的最小闭环,在此基础上叠加检索优化、题型路由、记忆压缩等策略,逐步逼近上限。 +07 +AFACEO 6 +金融智能创新大赛 +默认A +submission a.csv +赛题提交格式 +7 + +--- + +## 第 8 页 + +01题目背景与解析 /1.5 Baseline Token 消耗分析 +08 +Baseline Token 消耗分析 +核心数据概览(100题A榜) +总 Token 消耗 +376,836 +占500万预算的7.5% +Prompt Token 分布特征 +统计项 +最小 Prompt +中位数 +最大 Prompt +超过 4K 的题数 +数值 +211 +1,952 +7,088 +23 /100 +Datawhale C +)AFACEO 6 +金融智能创新大赛 +平均每题消耗 +3,768 +Prompt 2,403 + Completion 1,365 +剩余预算 +462万+ +还有 92.5%的预算空间可用 +说明 +短文档/简单题,几乎无上下文 +一半题目 prompt 不到2000 +多文档题叠加导致 +仅少数多文档题触发截断 +关键洞察 +1.Token 使用非常克制 +48 题集中在1000-2000 token 区间,多数文档不长,4000 截断很少触发 +2. 预算利用率极低 +只用了 7.5%,大量空间可加检索、多轮推理、自检等策略 +3. 多文档题是消耗大头 +仅23题超过 4Ktoken,这些是需要重点优化的场景 +结论:Baseline 的token 消耗极其保守,准确率提升的空间远大于token 预算的约束。放心加策略。 +8 + +--- + +## 第 9 页 + +## Chapter Two +数据理解与预处理 +Data Understanding and Preprocessing +### 2.1 五大金融文档领域与题型特征 +2.2 +2.3 +PDF 解析方案与检索策略 +数据预处理管线 +02 +6 + +--- + +## 第 10 页 + +02数据理解与预处理/2.1 五大金融文档领域 +五大金融文档领域与题型特征 +领域 +文档 +保险条款 +16 +监管法规 +26 +金融合同 +14 +财务报表 +10 +行业研报 +20 +五种题目能力标签 +国公式计算 +提取公式代入参数计算 +题目 +20 +20 +20 +20 +20 +文档特征 +条款密集、数字精确、结构规整 +法条编号、跨文档引用、表述严谨 +结构化条款、财务术语密集 +表格密集、数字精确、年度对比 +长篇论述、趋势分析、观点混合 +G9跨文档对比 +对比多份文档同类数据 +考查要点 +保险金计算、退保金额、等待期、免责条款 +法条引用、合规义务、跨文档对比 +债券条款、发行信息、信用评级条件 +年报指标、经营现金流、研发投入 +趋势判断、公司对比、数据核验 +联合推理 +多文档信息拼合综合判断 +Datawhale +领域分布:文档数 vs 题目数 +30- +20 - +10- +0- +16 +保险条款 +条款定位 +精确找到特定章节规定 +26 +20 +监管法规 +五大金融文档领域分布 +20 +08 +AFACEOe6 +金融智能创新大赛 +文档数 +题目数 +20 +20 +金融合同 +10 +财务报表 +Q事实查找 +定位带限定词的具体数字 +行业研报 +关键判断:每种类型对检索精度和上下文结构的要求不同,不能使用同一套提示词模板和检索策略处理所有题目。需按题型路由差异化处理。 + +--- + +## 第 11 页 + +02 数据理解与预处理/2.2 PDF 解析与检索策路 +09 +PDF 解析方案与检索策略 +PDF解析方案对比 +方案 +PyMuPDF +Datawhale +AFACEO 6 +金融智能创新大赛 +MinerU +优势 +纯CPU、毫秒级/页、速度快 +VLM+OCR 双模型、扫描件友好 +局限 +无OCR、表格还原度一般 +需GPU、架构复杂 +适用文档 +保险条款、监管法规(文本为主) +财务报表、金融合同(表格密集) +检索策略(无 Embedding/Rerank) +A正刻匹配 +精确匹配数字、百分比、日期、条款编号,零 +token +A BM25 关键词 +词频统计相关性排序,纯CPU,中文分词后稳 +定 +文档结构定位 +利用标题层级、条款编号、页码精确定位 +元数据预筛选 +按领域、文档标识、页码范围过滤 +A榜专属优化 +已知 doc_ids 一离线深度结构化提取一零token 消耗:条款编号映射表/财务指标结构化表/法条引用关系图 +关于分块:agent 自带 grep 工具可直接全文关键词定位。BM25+滑动窗口提取不需分块;保留文档结构时按条款/章节自然边界切分即可,不必纠结块大小。 +8 + +--- + +## 第 12 页 + +02数据理解与预处理/2.3数据预处理管线 +数据预处理管线 +从原始压缩包到结构化 Markdown 的全流程 +- Datawhale +10 +AFACEO 6 +金融智能创新大赛 +public_dataset_a.zip +赛方原始压缩包 +解压ZIP +跳过文件名过长文件 +数据预处理管线 +raw_dataset/ +questions/+raw/ +raw/下各领域 PDF +insurance/regulatory... +pymupdfalm +按页转Markdown +ProcessPool 并行 +关键设计决策 +解析工具:pymupdf4llm,纯CPU,按页转 Markdown +并行处理:ProcessPool 并行加速,多领域同时处理 +输出结构:processed_pymupdf4llm/domain/doc_id/page_NNNN.md +A 榜优势:已知 doc_ids,可离线深度预处理,零 Token 消耗 +预处理的价值: +- 将 PDF 转为结构化 Markdown,保留文本和表格 +. 按页拆分便于后续精准检索和片段提取 +- 离线完成,不消耗500万 Token 预算 +核心原则:预处理是离线投资——花一次时间解析所有文档,答题阶段才能精准检索、高效调用。 +10 + +--- + +## 第 13 页 + +## Chapter Three +题型分类与问答策略 +Question Classification and QA Strategies +3.1 +3.2 +3.3 +题型分类与路由策路 +检索策略与提示词优化 +计算题处理与答案自检 +03 +9 + +--- + +## 第 14 页 + +03 题型分类与问答策略/3.1题型分类与路由 +11 +题型分类与路由策略 +推理类型 +直接事实查找 +条款定位+判断 +公式计算 +跨文档对比 +多文档联合推理 +type 字段质量因领域而异 +领域 +insurance +regulatory +contracts/reports +特征 +答案在文档中明确定义 +答案分散在条款多处 +需提取公式和参数执行计算 +对比多份文档同类数据 +多份文档信息拼合综合判断 +Datawhale C +推荐策略 +精准检索+直接输出,不需要推理链 +多片段检索+逐项验证 +简单运算直接推理;复杂计算(多位数乘除、链式百分比) Python执行 +分文档检索+结构化提取+代码计算 +多文档检索+逐条件验证+逻辑归并 +AFACEO 6 +金融智能创新大赛 +type 实际内容 +事实查询、推理判断、比较分析、计算题 +多选题、判断、mcq、tf(纯冗余) +混合:部分格式+部分领域标签 +可用性 +V可信 +X冗余 +丕需区分 +处理建议 +可直接映射到推理类型分类 +忽略,与 answer_format 完全一致 +结合 answer_format 和题目文本自行判定 +路由原则:有计算的必须走代码,纯判断走大模型;多选题和联合推理出错率更高,优先分配自检 token。 +10 + +--- + +## 第 15 页 + +03题型分类与问答策略/3.2检索与提示词策路 +检索策略与提示词优化 +理想管线:四步流程 +12 +Datawhale +AFACO G +金融智能创新大赛 +离线解析 +结构化预处理 +提示词优化核心技巧 +选项前置 +模型对上下文前部注意力更高,先看到选项再读文档匹配精度更好 +输出极简 +严格约束输出为纯答案字母,省掉推理链 token(复杂题可保留) +系统提示词归零 +能不放系统提示词就不放,每道题重复数百次累积浪费可观 +零 Token 召回 +BM25/正则/结构定位 +极简提示词 +选项前置+片段+问题 +单次模型调用 +输出答案字母 +按题型差异化模板 +多选题:逐项验证模板,对每个选项单独确认文档是否支持 +计算题:提取公式+提取参数模板,数学运算交 Python +判断题:正误对照模板,同时给支持和不支持的证据 +事实查找:精准定位模板,直接给出答案 +A 关键警告:实验表明,粗糙检素(切片破坏连贯性)准确率反而低于硬截断。好的检索需要切分不破坏语义完整性。 +11 + +--- + +## 第 16 页 + +03题型分类与问答策略/3.3计算题与自检 +计算题处理与答案自检 +计算题处理管线 +13 +Datawhale +AFACEO 6 +金融智能创新大赛 +检索文档 +提取公式和参数 +生成代码 +Python 计算脚本 +执行计算 +获取精确结果 +匹配选项 +输出答案字母 +×注意:简单一步运算(加减、小数值乘除)可直接推理;多位数乘除、链式百分比、多步嵌套计算必须走 Python 代码执行 +答案自检策略 +场景一:Token 余粮充足 +每题1000-2000 token,100题只用10-20万 +对多选题、公式计算等高错误率题型做一轮自检 +场景二:Token 接近上限 +不做自检,先保证每道题正常答完 +比多确认一遍更实际 +场景三:Token 已达上限(罚无可罚) +TokenScore 归零且不再降 +放开 all-in:自检、多次采样、换策略重试 +核心原则:计算交给代码,理解和提取交给大模型。自检不是“应不应该”的问题,是"有没有余粮"和”罚无可罚后要不要 all-in"的问题。 +12 + +--- + +## 第 17 页 + +## Chapter Four +Agent上下文管理 +Agent Context Management +### 4.1 从提示词工程到上下文工程 +### 4.2 记忆系统架构与压缩策路 +4.3 +Token 预算与答题策略 +04 +14 + +--- + +## 第 18 页 + +04Agent上下文管理/4.1上下文工程理论框架 +从提示词工程到上下文工程 +15 +Datawhale +范式升级:2023-2024提示词工程(优化单次指令)一2025上下文工程(管理系统性上下文供给)。模型能力越过阈值后,输出不及预期的主因是上下文缺失而非能力不足。 +“上下文窗口有限时,住里面放什么信息、怎么组织,对模型输出的影响超过了提示词本身。“—— Karpathy.2025 +上下文的三类组成 +AFACEOe6 +金融智能创新大赛 +指导性上下文 +信息性上下文 +行动性上下文 +告诉模型做什么、怎么做 +系统提示词、任务描述、少样本示例、输出格式 +一应该极简 +告诉模型需要知道什么 +检索结果、对话历史、长期记忆 +应该精准且精炼 +告诉模型能做什么、做了什么 +工具定义、工具调用历史、返回结果 +一代码执行用于计算 +上下文退化的四种失效模式 +上下文污染 +幻觉进入上下文导致后续推理被污染 +上下文干扰 +无关信息覆盖模型训练知识导致降智 +上下文混淆 +冗余不相关内容让输出偏离期望 +上下文冲突 +上下文中的信息互相矛盾 +四个操作原语 +写入Write +选取 Select +压缩 Compress +隔离 Isolate +14 + +--- + +## 第 19 页 + +04Agent上下文管理/4.2记忆系统与压缩策路 +记忆系统架构与压缩策略 +三层记忆架构+结构化工作记忆(按文档分节维护,每题增量更新) +工作记忆 ~内存(RAM) +当前上下文窗口中的内容 +会话级易失,窗口管表达 +16 +Datawhale +AFACEO G +金融智能创新大赛 +短期记忆 ~磁盘缓存 +当前会话的压缩历史 +会话内持久,增量写入 +长期记忆 ~硬盘归档 +跨会话的知识、事实和偏好 +持久存储,结构化索引 +“丢失在中间“效应 (Lost in the Middle) +- 大模型对上下文的理解呈 U 型曲线:开头和结尾最好,中间显著衰减 +- 模型越大,U型越明显;即使无干扰信息,token增加推理质量也下降 +- "100 万上下文窗口"更多是检索能力,不是全量理解能力 +四层过滤管线 +元数据预过滤 +按领域/文档标识筛选 +关键词粗筛(BM25) +精确匹配数字/术语 +实践建议 +- 最重要信息放开头和结尾 +- 一个窗口不超过5个片段 +- 3个精准片段>10 个粗糙片段 +- 检索为主、压缩为辅 +文档结构定位 +标题层级/条款编号跳转 +送入大模型 +仅3个精锐片段 +生产级压缩体系(五层递进,能不用大模型就不调用) +清除过期缓存 +剥离旧消息 +空闲60分钟自动清理 +保留细粒度上下文 +多Agent隔离:子Agent独立处理各自文档,只向主控返回结构化摘要,避免信息混淆 +读时投影 +移除已归档消息 +结构化记忆替代 +零模型调用 +大模型全量压缩 +回退路径 +15 + +--- + +## 第 20 页 + +04Agent上下文管理/4.3 Token 预算与答题策略 +Token 预算分配与答题策略设计 +最优估算:单题Token 消耗 +-1,000 +单题token(检索3片段) +-10万 +A榜100题总消耗 +答题路由设计 +题型 +事实查找/条款定位 +公式计算 +跨文档丿联合推理 +独立回答 vs连续推理复用 +独立回答(推荐) +每题从零开始,通过结构化工作记忆跨题复用已验证信息 +管线 +单次问答管线 +代码执行管线 +Agent管线 +17 +Datawhale +AFACEO 6 +金融智能创新大赛 +3.5×+ +相比硬截断的优化空间 +-100万 +复杂题多步推理上限 +400万+ +可大胆投入的余量 +策略 +BM25 检索 —极简提示词输出答案 +检索提取 Python 计算>匹配选项 +多步检索逐条件验证逻辑归并 +子 Agent 隔离回答 +跨文档对比题各子 agent处理一份文档,主控只接收精炼结果 +16 + +--- + +## 第 21 页 + +05 总结与核心判断 +总结与核心判断 +18 +AFACEO 6 +金融智能创新大赛 +1 +上下文工程优先于提示词工程 +构建动态上下文供给系统,让模型每次只看到最需要的信息 +4 +2 +检索是最好的压缩 +把几十页文档浓缩成几个精准片段,且不消耗token +5 +Datawhale +TokenScore 是线性的,不要焦虑 +把token 花在能提升准确率的地方,哪怕多花一些也是对的决策 +上下文窗口是内存,记忆系统是硬盘 +只在需要时加载相关信息到工作记忆,不试图塞满所有内容 +3 +不要让大模型做不擅长的事 +数学计算交给代码,精确匹配交给关键词,语义理解才交给模型 +6 +写入/选取/ 压缩/隔离 +四个操作原语是上下文工程的通用语言,适用于竞赛到生产全场景 +真正拉开差距的永远是准确率。TokenScore线性无拐点,把token花在能提升准确率的地方,哪怕多花也是对的决策。 +"多数agent 的失败,根源在上下文工程的失败,而非模型能力的不足。” +-- Karpathy, 2025 +17 + +--- + +## 第 22 页 + +拿下赛题四 +上下文工程,是 Agent 系统的核心战场 +扫码加入赛题交流群,获取更多实战干货 +AFAC +钉钉赛题交流群 +AFAC公众号 +AFAC 大赛小助手 +Datawhale +& +A +人FAC口G +金融智能创新大赛 +18 + +--- diff --git a/reference/AFAC2026_OFFICIAL_RULES.md b/reference/AFAC2026_OFFICIAL_RULES.md new file mode 100644 index 0000000..0278810 --- /dev/null +++ b/reference/AFAC2026_OFFICIAL_RULES.md @@ -0,0 +1,442 @@ +# AFAC2026 挑战组一赛题四：金融长文本 Agent 的动态记忆压缩与高效问答挑战 + +> 来源：天池官方赛事页面 +> 页面：https://tianchi.aliyun.com/competition/entrance/532486/information +> 转换日期：2026-07-19 +> 说明：以下内容由官网规则 HTML 转换为 Markdown，作为本项目的最高优先级赛事规则参考。 + +![赛事规则图片](https://static.tianchi.aliyun.com/tianchi/race/images/prod/532486/1111483656572074/rf7hi2oq4yzkdfdo.jpg) + +## 一、任务介绍 + +金融行业存在大量专业性强、篇幅长、结构复杂的文本资料，例如保险条款、监管法规、债券募集说明书、上市公司年报和行业研究报告。这类文档通常包含精确的期限、比例、例外条件、权利义务关系和跨章节引用，单纯依赖通用知识或关键词匹配难以稳定回答问题。 + +随着大语言模型和检索增强生成技术的发展，金融场景对模型的要求已经从“能否阅读单段文本”扩展到“能否在多份长文档之间定位证据、比较差异、完成计算和判断”。模型不仅需要准确检索相关片段，还需要理解金融语义、识别干扰项，并在答案格式上保持严格可评测。 + +然而，在实际应用中，金融长文本的篇幅往往远超模型上下文窗口的经济使用区间。直接将全文输入模型会导致 Token 消耗量巨大，推理成本难以控制；简单截断或朴素摘要又容易丢失关键事实、业务逻辑和约束条件，造成事实性错误或推理链断裂。 + +本赛题面向金融长文档问答任务，要求参赛者在不修改基座模型参数、仅通过Qwen系列模型 API 调用的前提下，设计 Agent 层面的记忆流转、动态压缩和上下文优化策略。赛题围绕五类典型金融文本构建评测集，考查系统在多文档阅读、事实核验、复杂推理、跨文本比较、答案规范化和 Token 成本控制方面的综合能力。 + +## 二、任务目标 + +参赛者需要构建一个金融长文本 Agent，出题方提供金融长文本材料和配套问题集，参赛者需要设计并实现 Agent 的上下文管理、信息压缩、证据检索和问答策略，使系统在尽可能少的 Token 消耗下准确回答问题。 + +正式线上评测以answer.csv形式提交。参赛者需根据评测要求生成答案文件，并附带 Token 消耗统计。出题方负责提供评测文本和问题，参赛者需调用Qwen系列模型 API 完成推理，不得使用任何其他开源或闭源模型替代，参赛者仅限通过阿里云百炼平台或魔搭社区调用Qwen系列模型API完成推理。 + +题目类型包括： + +- 单选题：从 A、B、C、D 中选择唯一正确答案。 + +- 多选题：从 A、B、C、D 中选择所有正确答案。 + +- 判断题：使用 A/B 表示正确或错误，具体含义以题目文件中的选项为准。 + +赛题重点考查以下能力： + +- 金融长文档阅读理解：从 PDF 或清洗后的文本中读取有效信息。 + +- 多文档检索与定位：根据题目自动找到相关文档和证据段落。 + +- 跨文档比较分析：比较不同产品、法规、公司或报告中的条款与指标。 + +- 数值计算与条件推理：处理比例、金额、期限、现金流、责任范围等信息。 + +- 动态记忆压缩：在多轮读取和推理过程中保留关键事实，压缩冗余上下文。 + +- Token 成本优化：减少无效全文输入、重复检索和冗余模型调用。 + +- 抗干扰能力：在相近表述、细微条件变化和复杂选项中识别正确答案。 + +可采用的技术方向包括动态摘要抽取、Prompt Engineering、思维链提示、Agent 记忆管理、上下文窗口优化、检索增强生成（RAG）、结构化信息抽取和答案后处理等。 + +## 三、任务数据 + +### 3.1 数据领域 + +本数据集覆盖五个金融长文档领域： + +| **领域标识** | **领域名称** | **文档数量** | **A 榜题数** | **B 榜题数** | **主要考查点** | +| --- | --- | --- | --- | --- | --- | +| insurance | 保险条款 | 16 | 20 | 20 | 保险责任、身故保险金、退保金额、领取规则、条款比较 | +| regulatory | 监管法规 | 26 | 20 | 20 | 法规适用、合规义务、时限要求、监管处罚、条文优先级 | +| financial_contracts | 金融合同 | 14 | 20 | 20 | 债券条款、发行信息、评级信息、权利义务关系 | +| financial_reports | 财务报表 | 10 | 20 | 20 | 年报指标、经营表现、现金流、研发投入、分红政策 | +| research | 行业研报 | 20 | 20 | 20 | 行业趋势、公司比较、指标解读、研究结论核验 | + +全量基准题共 200 道，其中 A 榜 100 道，B 榜 100 道。 + +### 3.2 A/B 榜设置 + +A 榜题目提供相对明确的文档关联信息，适合用于开发、调试和验证模型的基础阅读能力。 + +B 榜题目难度更高，题目文件不直接给出 doc_ids，要求系统先完成候选文档检索，再进行阅读理解和答案判断。B 榜更接近真实金融问答场景中的盲测设置。 + +A榜测评结束后将启动B榜测评环节。参赛者须在A榜取得有效成绩，方可获得进入B榜测评的资格。 + +- A榜数据评测：2026年6月8日10:00-7月21日20:00 + +- B榜数据评测：2026年7月22日0:00-7月24日17:00 + +### 3.3 题目格式 + +问题文件中的单条样例如下： + +```json +{ + "qid": "fin_a_001", + "domain": "financial_reports", + "split": "A", + "question": "根据比亚迪连续两年的年度报告，下列关于公司经营业绩变化的描述中，哪些是准确的？", + "options": { + "A": "2025 年营业收入较 2024 年实现增长", + "B": "2025 年归属于上市公司股东的净利润出现下滑", + "C": "2025 年经营活动产生的现金流量净额优于 2024 年", + "D": "2025 年研发投入占营业收入的比例较 2024 年有所下降" + }, + "answer_format": "multi", + "type": "财务指标对比分析", + "doc_ids": [ + "byd_2024_annual", + "byd_2025_annual" + ] +} +``` + +字段说明： + +| **字段** | **类型** | **说明** | +| --- | --- | --- | +| qid | String | 题目唯一编号 | +| domain | String | 所属领域 | +| split | String | 所属组别，取值为 A 或 B | +| question | String | 题干 | +| options | Object | A/B/C/D 四个候选选项 | +| answer_format | String | 题型，包含 mcq、multi、tf | +| type | String | 题目能力标签或业务类型 | +| doc_ids | Array[String] | 参考文档编号，A 组提供，B 组不提供 | + +### 3.4 文档元数据 + +每条文档元数据包含 doc_id、path、title 等字段。系统应根据 doc_id 找到对应原始 PDF，再用于题目作答。 + +评测长文本来源包括公开金融监管法规、公司合规指南、财务报告、保险条款与产品说明书、金融合同、法律文档和行业研究报告等材料。文本经过整理和审阅后用于评测，并刻意保留较长篇幅和分散关键信息点，以考查 Agent 的压缩、检索和跨段推理能力。 + +## 四、提交格式 + +线上评测answer.csv 应同时包含每道题答案和本次评测的 Token 消耗统计。 + +示例： + +| qid | answer | prompt_tokens | completion_tokens | total_tokens | +| --- | --- | --- | --- | --- | +| summary | | 3627557 | 629 | 3628186 | +| ins_a_001 | B | 37201 | 1 | 37202 | +| ins_a_002 | A | 33063 | 1 | 33064 | + +字段说明： + +| **字段** | **类型** | **说明** | +| --- | --- | --- | +| answers | Object | 题目编号到答案字符串的映射 | +| prompt_tokens | Number | 回答全部评测问题过程中消耗的输入 Token 数 | +| completion_tokens | Number | 回答全部评测问题过程中消耗的输出 Token 数 | +| total_tokens | Number | completion_tokens与prompt_tokens之和 | + +答案格式要求： + +- 单选题：提交一个大写字母，例如 A。 + +- 判断题：提交一个大写字母，例如 A 或 B。 + +- 多选题：提交多个大写字母，按字母顺序排列，不使用空格、逗号或其他分隔符，例如 ABC。 + +- 若答案为空、包含非法字符、顺序不规范或与标准答案不完全一致，则该题计为错误。 + +## 五、评测指标 + +本赛题采用“准确率为主、Token 效率为辅”的实时综合评分方式。系统首先根据参赛者提交的 answer.csv 与标准答案逐题比对，计算准确率 Accuracy = Correct / Total。参赛者需在提交文件中提供本次作答过程的 Token 消耗统计，即 summary.total_tokens。 + +为保证线上评测能够实时出分，采用固定预算归一化方式计算。设 TokenBudget = 5,000,000，TotalTokens 为参赛者提交的总 Token 消耗，则： + +TokenScore = max(0, min(1, (TokenBudget - TotalTokens) / TokenBudget)) + +FinalScore = 100 * Accuracy * (0.7 + 0.3 * TokenScore) + +其中，准确率决定主体得分，Token 效率最多影响 30% 的加权系数。若参赛者未提交有效 Token 统计或 TotalTokens <= 0，则 TokenScore 记为 0。最终分数不低于 0。 + +若综合得分相同，依次按以下规则排序： + +- 准确率更高者排名靠前。 + +- 若准确率仍相同，TotalTokens 更低者排名靠前。 + +- 若仍相同，提交时间更早者排名靠前。 + +评测时会对答案字母进行标准化处理，核心规则如下： + +- 单选题和判断题以首个有效答案字母为准。 + +- 多选题会将答案字母去重并排序后，与标准答案做完全匹配。 + +- 多选题不设置部分分，漏选、错选、多选均计为错误。 + +- Token 统计覆盖回答评测问题全过程中的所有模型 API 调用，包括检索摘要、上下文压缩、证据判断、答案生成和自检等步骤。 + +平台可按领域、组别、准确率和 Token 消耗统计分项表现，用于分析系统在不同金融文本类型上的能力差异和成本收益表现。 + +特别注意：本次评测基准模型采用Qwen3.6-plus模型。 + +#### 模型使用限制 + +1.**数据预处理 / 文档解析阶段** +允许使用多种工具，也允许使用非 Qwen 系列模型，例如 OCR、版面分析、表格恢复、阅读顺序还原、PDF 转结构化文本等。像 MinerU 这类方案虽然包含非 Qwen 模型，但主要作用是将文档解析为更可读、更结构化的输入，因此属于允许的预处理工具范围。 + +2.**推理问答阶段** +围绕题目进行文档定位、段落检索、记忆压缩、证据判断和答案生成时，应严格使用 Qwen 系列模型。 +**不得使用任何embedding模型进行检索和推理** + +3.**边界限制** +预处理阶段使用的非 Qwen 模型能力，仅限于文档解析和结构化处理，不得延伸到正式检索和推理流程。具体包括： + +- 不得使用非 Qwen 模型生成的向量、排序结果或召回结果参与正式答题； + +- 不得使用非 Qwen 模型参与 rerank、候选过滤、答案投票或纠错； + +- 不得直接使用预处理阶段生成的语义摘要、FAQ、结论提炼、知识库等结果参与正式答题。 + +#### 代码审核 + +B 榜成绩排名前 15 名的参赛者需提交完整的代码和复现文档，包括但不限于以下内容： +submission.zip +├── answer.csv +├── evidence.json +├── processed_data/ # 清洗、切分、结构化后的长文本数据 +├── agent/ # Agent 系统完整代码 +├── script/ # 可复现运行脚本，包括数据处理、索引构建、检索、记忆压缩、Agent推理及结果生成 +├── logs/ # 实验记录 +├── requirements.txt +└── README.md +参赛者提交的内容应支持按照说明文档一键运行，并生成符合要求的结果文件。提交的代码压缩包总大小不超过1GB。 + +## 六、Baseline 参考 + +基于 Qwen-plus 的基线评测结果： + +| **组别** | **正确/样本** | **准确率** | **Token 消耗** | +| --- | --- | --- | --- | +| A 组 | 49/100 | 49.0% | 2,991,883 | +| B 组 | 13/100 | 13.0% | 3,884,045 | +| 总计 | 30/200 | 15.0% | 6,875,928 | + +该结果说明，仅将长文档直接输入通用模型并不能稳定解决任务。参赛者可重点优化以下方向： + +- 文档解析质量：提升 PDF 文本抽取、表格处理和版面结构还原能力。 + +- 检索策略：构建面向金融术语、条款编号和指标名称的混合检索。 + +- 证据聚合：支持同题多文档、多段落证据的联合判断。 + +- 答案约束：对模型输出进行字母抽取、排序、去重和合法性检查。 + +- 领域知识增强：针对保险、监管、合同、财报、研报分别设计提示词和后处理规则。 + +## 七、样例任务 + +以监管法规领域为例，题目给出一个金融机构客户尽职调查或受益所有人识别的具体业务场景，要求系统结合法规施行日期、识别义务、报告时限和管控措施判断多个选项是否合规。此类题目要求模型严格依据法规条文作答，不能用常识替代证据。本部分将在下方以真实题目 reg_a_014 为例，详细展示监管法规领域的典型案例及证据可追溯性规范。 + +以保险条款领域为例，题目可能给定多款产品的已交保费、现金价值、账户价值、领取状态等条件，要求比较各产品身故保险金或退保金额。系统需要同时理解条款触发条件和计算公式。 + +本赛题的监管法规领域（regulatory）题目要求模型严格基于给定的法律法规文本进行多选项的合规性判断和法条追溯。以下给出一个典型的多选题案例及解析过程，用于阐明问题定义、证据定位和答案生成逻辑，同时说明系统在输出时需满足的证据可追溯性要求。 + +#### 1. 题目信息 + +题目ID：reg_a_014 + +领域标签：regulatory（监管法规） + +题型：多选题（multi） + +关联文档ID：strict_csrc_023（《上市公司治理准则》）、strict_csrc_035（《上市公司章程指引》） + +题干： + +某A股上市公司（非金融类，总股本5亿股，最近一期经审计净资产120亿元）拟进行如下四项决策： + +① 为资产负债率为75%的全资子公司提供8亿元担保； + +② 将原募投项目“智能工厂建设”剩余募集资金6.5亿元变更为“新能源材料研发中心”； + +③ 选举新一届独立董事，其中1名候选人系某律所合伙人，其所在律所过去12个月内曾为公司提供常年法律顾问服务但未承办重大诉讼； + +④ 审议《关于修订公司章程第42条股东大会特别决议事项的议案》，拟将“对外提供财务资助”纳入须经特别决议的范围（原章程未列明）。 + +根据《上市公司治理准则》《上市公司章程指引（2023年修订）》及相关证券监管规定，下列关于上述事项是否必须经股东大会审议及表决方式的判断，正确的有？ + +选项： + +A. 事项①中，因被担保方资产负债率超过70%，且担保金额占公司最近一期经审计净资产比例达6.67%，两项任一条件触发即须经股东大会审议通过，故该项担保必须提交股东大会审议。 + +B. 事项②中，变更募集资金用途涉及金额6.5亿元，虽未达净资产10%（12亿元），但因变更投向属于“主营业务方向重大调整”，根据《章程指引》第44条及《治理准则》第19条，仍须经股东大会特别决议通过。 + +C. 事项③中，该独立董事候选人存在《治理准则》规定的“最近12个月内为上市公司提供过法律服务”的情形，构成独立性障碍，不得提名为独立董事候选人，故股东大会不得对其行使选举权。 + +D. 事项④中，公司章程自主增设特别决议事项不违反《章程指引》第77条“上市公司可在本指引基础上增加特别决议事项”的授权性规定，但须经股东大会普通决议通过即可生效，无需特别决议。 + +标准答案：AC + +#### 2. 法规证据定位 + +系统在作答此题时，应当在检索阶段定位到以下具体法条作为判断依据： + +证据一（对应事项①及选项A）： + +引用文件：strict_csrc_035（《上市公司章程指引》）第四十七条。 + +法条原文：“第四十七条 公司下列对外担保行为，须经股东会审议通过：... （四）为资产负债率超过百分之七十的担保对象提供的担保；...” + +证据二（对应事项②及选项B）： + +引用文件：strict_csrc_035（《上市公司章引》）第八十一条及第八十二条。 + +法条原文：第八十一条规定了由股东会以普通决议通过的事项，包括“（四）除法律、行政法规规定或者本章程规定应当以特别决议通过以外的其他事项”；第八十二条详细列举了须经特别决议通过的事事项，而“变更募集资金用途”未列入特别决议事项。 + +证据三（对应事项③及选项C）： + +引用文件：strict_csrc_023（《上市公司治理准则》）第四十一条。 + +法条原文：“第四十一条 独立董事的任职条件、选举更换程序等，应当符合有关规定。独立董事不得与其所受聘上市公司及其主要股东、实际控制人存在直接或者间接利害关系，或者其他可能影响其进行独立客观判断的关系。” + +证据四（对应事项④及选项D）： + +引用文件：strict_csrc_035（《上市公司章程指引》）第八十二条。 + +法条原文：“第八十二条 下列事项由股东会以特别决议通过：... （三）本章程的修改；...” + +#### 3. 答案逻辑推理过程 + +事项①分析：被担保对象为资产负债率75%的子公司，已超过“百分之七十”的法定界限。依据《章程指引》第四十七条第（四）款，该事项属于必须经股东会审议的范围，因此选项A正确。 + +事项②分析：变更募集资金用途属于股东会审议事项，但根据《章程指引》第八十一条及第八十二条，该项决议未被列为必须经特别决议通过的事项。根据第八十一条第（四）款，其属于应以普通决议（出席股东会表决权过半数）通过的事项。选项B声称“必须特别决议通过”，因此选项B错误。 + +题目ID：reg_a_014 +领域标签：regulatory（监管法规） +题型：多选题（multi） +关联文档ID：strict_csrc_023（《上市公司治理准则》）、strict_csrc_035（《上市公司章程指引》） + +题干： +某A股上市公司（非金融类，总股本5亿股，最近一期经审计净资产120亿元）拟进行如下四项决策： +① 为资产负债率为75%的全资子公司提供8亿元担保； +② 将原募投项目“智能工厂建设”剩余募集资金6.5亿元变更为“新能源材料研发中心”； +③ 选举新一届独立董事，其中1名候选人系某律所合伙人，其所在律所过去12个月内曾为公司提供常年法律顾问服务但未承办重大诉讼； +④ 审议《关于修订公司章程第42条股东大会特别决议事项的议案》，拟将“对外提供财务资助”纳入须经特别决议的范围（原章程未列明）。 + +根据《上市公司治理准则》《上市公司章程指引（2023年修订）》及相关证券监管规定，下列关于上述事项是否必须经股东大会审议及表决方式的判断，正确的有？ + +选项： +A. 事项①中，因被担保方资产负债率超过70%，且担保金额占公司最近一期经审计净资产比例达6.67%，两项任一条件触发即须经股东大会审议通过，故该项担保必须提交股东大会审议。 +B. 事项②中，变更募集资金用途涉及金额6.5亿元，虽未达净资产10%（12亿元），但因变更投向属于“主营业务方向重大调整”，根据《章程指引》第44条及《治理准则》第19条，仍须经股东大会特别决议通过。 +C. 事项③中，该独立董事候选人存在《治理准则》规定的“最近12个月内为上市公司提供过法律服务”的情形，构成独立性障碍，不得提名为独立董事候选人，故股东大会不得对其行使选举权。 +D. 事项④中，公司章程自主增设特别决议事项不违反《章程指引》第77条“上市公司可在本指引基础上增加特别决议事项”的授权性规定，但须经股东大会普通决议通过即可生效，无需特别决议。 + +标准答案：AC + +#### _2. _*法规证据定位* + +系统在作答此题时，应当在检索阶段定位到以下具体法条作为判断依据： + +证据一（对应事项①及选项A）： +引用文件：strict_csrc_035（《上市公司章程指引》）第四十七条。 +法条原文：“第四十七条 公司下列对外担保行为，须经股东会审议通过：... （四）为资产负债率超过百分之七十的担保对象提供的担保；...” + +证据二（对应事项②及选项B）： +引用文件：strict_csrc_035（《上市公司章引》）第八十一条及第八十二条。 +法条原文：第八十一条规定了由股东会以普通决议通过的事项，包括“（四）除法律、行政法规规定或者本章程规定应当以特别决议通过以外的其他事项”；第八十二条详细列举了须经特别决议通过的事事项，而“变更募集资金用途”未列入特别决议事项。 + +证据三（对应事项③及选项C）： +引用文件：strict_csrc_023（《上市公司治理准则》）第四十一条。 +法条原文：“第四十一条 独立董事的任职条件、选举更换程序等，应当符合有关规定。独立董事不得与其所受聘上市公司及其主要股东、实际控制人存在直接或者间接利害关系，或者其他可能影响其进行独立客观判断的关系。” + +证据四（对应事项④及选项D）： +引用文件：strict_csrc_035（《上市公司章程指引》）第八十二条。 +法条原文：“第八十二条 下列事项由股东会以特别决议通过：... （三）本章程的修改；...” + +#### _3. _*答案逻辑推理过程* + +事项①分析：被担保对象为资产负债率75%的子公司，已超过“百分之七十”的法定界限。依据《章程指引》第四十七条第（四）款，该事项属于必须经股东会审议的范围，因此选项A正确。 + +事项②分析：变更募集资金用途属于股东会审议事项，但根据《章程指引》第八十一条及第八十二条，该项决议未被列为必须经特别决议通过的事项。根据第八十一条第（四）款，其属于应以普通决议（出席股东会表决权过半数）通过的事项。选项B声称“必须特别决议通过”，因此选项B错误。 + +事项③分析：候选人所在律所在过去12个月内为上市公司提供过常年法律顾问服务。依据《治理准则》第四十一条，该候选人与上市公司存在利害关系，影响其独立客观判断，构成独立董事独立性缺陷，不能提名为候选人。因此选项C正确。 + +事项④分析：将“对外提供财务资助”纳入特别决议范围，其实质是对公司章程中关于股东会决策程序条款的修改。依据《章程指引》第八十二条第（三）款，“本章程的修改”必须经股东会特别决议（出席股东表决权2/3以上）通过方可生效。选项D声称“经普通决议通过即可生效”，与此规定抵触，因此选项D错误。 + +综合上述推理，正确答案为AC。 + +#### _4. _*证据可追溯性输出规范* + +为了保证回答的真实性和可审核性，系统除返回标准答案外，还应当在输出结构中附带证据定位元数据。其格式规范示例如下： + +```json +{ + "qid": "reg_a_014", + "answer": "AC", + "evidence_retrieval": [ + { + "doc_id": "strict_csrc_035", + "quoted_clause": "第四十七条 公司下列对外担保行为，须经股东会审议通过：... （四）为资产负债率超过百分之七十的担保对象提供的担保；", + "reasoning": "全资子公司的资产负债率为75%，超过70%限额，根据第四十七条第四款规定，此担保必须由股东会审议通过。支持选项A。" + }, + { + "doc_id": "strict_csrc_023", + "quoted_clause": "第四十一条 ...独立董事不得与其所受聘上市公司...存在直接或者间接利害关系...", + "reasoning": "独立董事候选人所在律所过去12个月内曾为公司提供法律顾问服务，构成独立性受限情形。支持选项C。" + }, + { + "doc_id": "strict_csrc_035", + "quoted_clause": "第八十二条 下列事项由股东会以特别决议通过：... （三）本章程的修改；", + "reasoning": "将对外财务资助纳入特别决议范围属于修改章程行为，必须经出席会议的股东所持表决权的三分之二以上（特别决议）通过。选项D判断不正确。" + } + ] +} +``` + +## 八、推荐解题流程 + +一个较稳健的系统可以采用以下流程： + +- 文档预处理：抽取 PDF 文本，保留标题、页码、章节、表格和条款编号。 + +- 文档组织与索引：按领域构建关键词索引和结构化字段索引；允许为文档解析和结构化处理服务的预处理结果，但不得将非 Qwen 模型产生的语义召回能力延续到正式检索阶段。 + +- 题目解析：识别题型、关键词、实体、时间、金额、指标和候选文档。 + +- 证据检索：根据题目和选项分别检索证据，必要时进行多轮查询。正式答题阶段的检索与推理应严格在规定模型和规则范围内完成。 + +- 推理作答：让模型基于证据逐项判断选项真假。 + +- 答案校验：根据题型约束输出合法答案字母。 + +- 结果汇总：生成符合提交格式的 csv或json文件。 + +## 入围规则 + +1.B榜成绩排名前15名，并由组委会通知进入报告提交的队伍，需按规定提交指定材料。 + +2.未按时提交或者审查过程中发现有违反比赛规则的队伍将被取消评审和获奖资格。如有队伍被取消评审和获奖资格，将根据天池平台榜单成绩排名递补。 + +## 九、注意事项 + +1.参赛者在分析数据和构建模型时，需遵循国家法律法规、行业规定和道德规范，不得侵犯他人知识产权、隐私权等权益； + +2.参赛者在比赛过程中，建议与其他参赛者积极互动，分享经验和心得，共同提高参赛水平； + +3.参赛者在比赛结束后，需同意将优秀作品和解决方案分享给其他参赛者，以便于大家共同学习和进步； + +4.参赛者须严格遵守比赛规则与公平竞赛原则，严禁以任何形式弄虚作假或作弊。一经查实存在违规行为，大赛组委会有权立即取消其参赛资格及成绩，并全额追回已发放的奖金及相关奖励； + +5.组织者保留对本次比赛的最终解释权。 + +6.参赛选手请添加官方赛题答疑群，请钉钉扫描二维码加入（或搜索钉钉群号：174890011923） + +![赛事规则图片](https://static.tianchi.aliyun.com/tianchi/race/images/prod/532486/1071584497862555/lb6eq6ta16i2nasg.png) + diff --git a/reference/advanced.md b/reference/advanced.md new file mode 100644 index 0000000..28c782d --- /dev/null +++ b/reference/advanced.md @@ -0,0 +1,800 @@ +目标：帮助参赛者快速理解赛题、跑通第一个可提交的基线方案，并明确后续优化方向。本 baseline 源码位于项目根目录 `src/` 下。题型分析、RAG 概念、金融文档背景知识和进阶优化方案请参考 进阶教程。 + +赛事官方地址：[https://tianchi.aliyun.com/competition/entrance/532486](https://tianchi.aliyun.com/competition/entrance/532486) + +## 1 赛题速览 + +### 1.1 任务定义 + +赛道四的核心任务是**金融长文本问答**。出题方提供五类金融文档和配套的选择题，要求参赛者构建 Agent 系统，在控制 Token 消耗的前提下准确作答。 + +赛事官方地址：[https://tianchi.aliyun.com/competition/entrance/532486](https://tianchi.aliyun.com/competition/entrance/532486) + +大部分题目涉及跨文档对比、公式计算、条款推理和逻辑判断，需要系统具备信息定位、知识理解和推理计算三层能力。 + +### 1.2 关键约束 + +- **模型限制**：正式提交只准调用 Qwen 系列模型 API + +- **题目范围**：A 榜题目附带 `doc_ids`，系统只需在指定文档中定位答案，不需要全网搜索 + +- **答案格式**：单选（mcq）、多选（multi）、判断（tf）三种 + +- **答案标准化规则**：单选题和判断题以首个有效答案字母为准；多选题将答案字母去重并排序后做完全匹配，**不设置部分分，漏选、错选、多选均计为错误** + +- **Token 预算**：500 万 Token 总量限制，评分兼顾准确率与 Token 效率 + +### 1.3 评分机制：准确率优先 + +最终得分公式大致为 `准确率 × (0.7 + 0.3 × Token 效率分)`。准确率权重占 70%，Token 效率占 30%。这意味着：即使你把 Token 压得极低，准确率上不去，总分也高不了；反之，准确率够高，Token 稍微超一点，对总分的影响有限。**因此优化的第一优先级是准确率，第二优先级才是 Token 效率。** + +### 1.4 数据分布 + +A 榜共 100 道题，5 个领域各 20 题，配套 86 份 PDF 文档： + +| 领域 | 题目数 | 配套文档数 | 文档特点 | + +| --- | --- | --- | --- | + +| 保险条款 | 20 | 16 | 条款密集，公式多，计算题集中 | + +| 监管法规 | 20 | 26 | 法条引用多，跨文档对比题多 | + +| 金融合同 | 20 | 14 | 募集说明书、债券条款，数据表格多 | + +| 财务报表 | 20 | 10 | 年报数据量大，财务指标对比题多 | + +| 行业研报 | 20 | 20 | 行业分析为主，事实查找题多 | + +题型分布上，多选题（multi）是重灾区，漏选或多选都不得分，对系统的精确度要求最高。单选题（mcq）和判断题（tf）相对简单，但跨文档的 mcq 题同样需要仔细阅读多份材料。 + +### 1.5 赛题特点解析 + +做这道题时，我们发现几个容易被低估的难点： + +**第一，doc_ids 只是范围提示，不是答案指针。** 题目告诉你看哪几份文档，但答案不会直接写在文档第一段。很多时候需要把 4 份条款的公式串起来计算，或者对比两份年报的同一指标才能判断对错。 + +**第二，金融知识门槛会过滤掉模型的常识推理。** 保险条款中的"等待期""免赔额""责任免除"，监管法规中的"受益所有人识别""客户尽职调查"，这些概念有严格的法律定义和适用条件。模型如果没有精准定位到对应条款，凭"常识"推断很容易选错。 + +**第三，题型之间差异大，没有统一解法。** 计算题需要提取公式后代入数值；跨文档对比题需要同时打开多份文档提取同一类数据；条款定位题需要精准找到"责任免除""等待期"等特定章节；事实查找题需要在几万字的研报中定位一个带年份限定的数字。用同一套策略处理所有题型，效果必然打折。 + +### 1.6 思维链（CoT）的实验发现 + +我们在实验中也测试过让模型"先推理再回答"的 CoT 策略：在 prompt 中要求模型对每个选项引用文档条款、说明判断依据，最后再给出答案。 + +效果是**两极分化**： + +- **对复杂题有提升**。比如跨文档对比题和多选题，模型被迫逐条验证，漏选和误选明显减少。部分计算题在 CoT 引导下，提取公式的准确率也有改善。 + +- **Token 消耗暴涨**。baseline 的平均 completion token 只有 2-5 个（模型直接输出字母），开启 CoT 后飙升到几百个。100 题跑下来，completion token 可能从 15 万涨到 100 万以上，对 500 万总预算的消耗非常可观。 + +结论：CoT 可以按需使用，不建议全量开启。在预算有限的前提下，更合理的策略是"按需开启"，只在计算题、多选题、跨文档对比题等复杂题型上启用 CoT，简单的事实查找题直接输出答案。这也是后续优化的一个方向。但是我建议都不要开，非必要的话。 + +### 1.7 关于框架选择 + +一个常见的参赛误区是：上来就用 LangChain、LlamaIndex 等现成框架搭建 Agent。这些框架为了通用性，在 prompt 模板、工具调用协议、中间状态记录上做了大量封装，**带来的直接后果是 Token 消耗会稍高于自定义设计的流程**，如果是大家都能获得答题准确率满分的背景下，后面大家应该是需要组自己设计如何组装prompt会更节省token的。 + +从评分公式看，准确率占 70%、Token 效率占 30%，两者都不是可以忽略的小头： + +- 如果参赛者的准确率都能做到不错（比如 60% 以上），这时排名差距主要来自 Token 效率。谁能在同等准确率下消耗更少的 Token，谁就能拿到更高的效率分。 + +- 如果整体准确率都不高（比如普遍低于 40%），这时排名差距主要来自准确率。Token 压得再低，准确率上不去也没有意义。 + +**真正榜单前列的选手，一定在准确率和 Token 消耗上做到了平衡。** 框架可以帮你快速搭建原型，但在正式提交前，建议把核心流程拆开重写，去掉不必要的 prompt 冗余和中间层封装，把 Token 预算用在最关键的地方。 + +赛题详细说明见 `赛题与数据.md`。 + +## 2 Baseline 定位 + +### 2.1 Baseline 概况 + +| 核心信息 | 信息详情 | + +| --- | --- | + +| 赛题任务类型 | 金融长文本问答（选择题：单选/多选/判断） | + +| baseline 代码 | [https://github.com/li-xiu-qi/afac2026-financial-qa](https://github.com/li-xiu-qi/afac2026-financial-qa) | + +| baseline 涉及的库 | openai、pyyaml、pymupdf4llm | + +| 所需环境和时间 | 环境：Python 3.10+、CPU 时间：15 分钟（含数据准备） | + +| baseline 分数 | 准确率 16.62%（A 组 100 题），Token 消耗 353,032（prompt 198,738 + completion 154,294） | + +| baseline 所使用的方法 | 硬截断 + 单轮 LLM 调用（no-tool 模式） | + +| 深入赛题会需要的知识点 | RAG 系统搭建、检索优化、Agent 决策、多轮推理 | + +本 baseline 是一个**最小可运行基线**（MVP）。它的设计哲学是先把端到端流程跑通，让参赛者能在 15 分钟内得到第一个可提交的 `submission.csv`，不追求高准确率。 + +**它能做的事：** + +- 读取 A 榜 100 题的 JSON 题目文件 + +- 根据 `doc_ids` 加载已解析的 Markdown 文档 + +- 将文档截断到 4000 字符，拼接为 prompt + +- 调用 LLM 生成答案，并规范化输出格式 + +- 并发处理 100 题，生成符合赛题要求的 JSON + CSV 结果 + +**为什么只用简单截断，不做检索** + +很多参赛者第一反应是"文档太长，应该先搜再读"。这个思路本身没错，但过早引入检索会带来一系列连锁问题。核心矛盾在于：**检索必然引入切片，而切片会破坏原文的连贯性**。 + +一段保险条款可能横跨三个自然段：前一段定义概念、中间一段给出公式、后一段附加例外条件。如果把这三段切成独立的片段分别检索，模型看到的可能只是"现金价值 = 累计保费 × 75%"，却看不到后面紧跟着的"因意外伤害导致的退保除外"。这种**连贯性断裂**是检索方案的根本风险。 + +从信息连贯性的角度看，**全文 > 摘要 > 检索片段**。全文让模型自己决定哪里重要，上下文天然完整；摘要虽然损失了细节，但至少保持了逻辑链条；检索片段则是最脆弱的，召回结果的质量直接决定了最终答案的质量，而单一检索策略（纯 BM25 或纯向量搜索）的召回质量往往不稳定。 + +RAG 系统里之所以要设计"粗排 + 细排"的两阶段架构，正是因为粗排（BM25 或向量相似度）召回的结果中，经常夹杂着不相关或弱相关的内容，甚至因为词频巧合、embedding 漂移等各种原因把无关片段排得很靠前。细排（rerank）的作用就是做二次筛选，把真正相关的片段提上来。但在baseline阶段，我们没有足够的调优经验来确定阈值、去重策略和融合权重，贸然引入检索反而可能让模型面对一堆零散、重复、缺上下文的片段，理解成本比直接读截断全文还高。 + +另外，检索不是加了就有效果的模块。它需要配合滑动窗口做片段边界保护、需要多路召回融合来互补 BM25 和向量搜索的盲区、需要动态阈值来过滤低质量片段。这些配套机制没有到位时，检索的提分潜力无法释放。 + +当然，直接截断也有自己的问题（前半截可能刚好没有答案），但这属于"信息够不够"的问题，比"信息对不对、连不连贯"更容易排查和迭代。baseline 阶段先固定一个稳定的上限，跑通端到端流程，后续再逐步引入检索并验证真实收益，是更务实的策略。 + +**实际得分验证：截断方案反而是最高分** + +下图是我们团队在 A 榜的提交记录： + +![](https://cdn.nlark.com/yuque/0/2026/png/69403934/1781683355905-ffd809bc-7e41-4b31-9c59-62a0dde9c604.png) + +硬截断 baseline 的得分与 Token 消耗： + +| 提交时间 | 方案 | 得分 | Token 消耗 | + +| --- | --- | --- | --- | + +| 21:54:50 | **硬截断 baseline** | **16.62** | 353,032 | + +我们也尝试过检索策略和混合策略的进阶方案，但得分均低于硬截断。一个可能的解释是，检索引入的切片破坏了原文连贯性，召回的片段中又混入了弱相关甚至不相关的内容，模型面对零散、缺上下文的片段，理解成本比直接读截断全文还高。另一个解释是，检索需要配合滑动窗口做片段边界保护、多路召回融合来互补盲区、动态阈值过滤低质量片段，这些配套机制没有到位时，检索的优势无法释放。 + +本 baseline 选择硬截断，是为了在 baseline 阶段先把端到端流程跑通、建立一个稳定的得分基准，再逐步引入检索并验证每一步的真实收益。这并不否定检索的价值。 + +**一个参考基准：赛题裸跑是什么水平** + +作为参照，赛题基于 Qwen-plus 做了一个最简单的基线：把长文档直接输入模型、不做检索也不做优化，A 组 100 题答对 17 道，准确率约 17%，Token 消耗约 362 万。这说明： + +- 本赛题确实不容易，裸跑模型只有约 17% 的准确率 + +- 本 baseline 的 16.62 分和这个裸跑基准处于同一水平，没有拖后腿 + +- 从零到 17 分不需要什么技巧，但从 17 分往上提分，每一步都需要精心设计 + +因此本 baseline 的策略是：**先把端到端流程跑通，用一个稳定的基准来衡量后续改动的真实收益**。如果你改了检索策略但分数没涨，至少知道不是基础流程的问题。 + +**它不做的事（留给后续版本）：** + +- 没有检索模块，直接截断文档前半部分 + +- 没有多轮推理，单轮提问、单轮回答 + +- 没有证据链追溯，无法解释为什么选某个答案 + +- 不做题目分类，所有题型用同一套 prompt + +**为什么选择 no-tool 而非 Function Calling** + +我们也测试过带 Function Calling（FC）的版本。FC 需要在每次请求时把工具声明（schema）放入 system prompt，让模型决定什么时候调用工具、传什么参数。实测 A 组 100 题的 Token 消耗对比如下： + +| 指标 | no-tool | tool（FC） | 差异 | + +| --- | --- | --- | --- | + +| prompt tokens | 198,738 | 273,226 | +37.5% | + +| completion tokens | 154,294 | 142,726 | -7.5% | + +| total tokens | 353,032 | 415,952 | +17.8% | + +FC 的 completion tokens 确实略少（因为输出被结构化成了 JSON），但 prompt _tokens 多了近 40%。原因是每道题的 prompt 都要携带工具声明，而且模型需要先生成 tool call 的 JSON，再等待外部函数执行结果，再发起第二轮请求。整个流程的 Token 开销和交互复杂度都更高。 + +本 baseline 采用 no-tool 方案，用更少的 Token 完成同一批题目，流程也更简单：读文档、拼 prompt、直接出答案，没有中间状态。 + +## 3 环境准备 + +### 3.1 基础环境 + +- Python 3.10+ + +- 操作系统：Linux / WSL / macOS + +### 3.2 安装依赖 + +``` +pip install -r requirements.txt +``` + +requirements.txt 中只包含 baseline 运行必需的依赖（`openai` 和 `pyyaml`）。如果你需要重新解析 PDF，再额外安装 `pymupdf4llm`。 + +### 3.3 数据准备 + +`data/` 目录已 gitignore，不纳入版本控制。你需要自行下载数据集并运行解析脚本生成。 + +**第一步：下载数据集** + +1. 访问赛事页面：[https://tianchi.aliyun.com/competition/entrance/532486](https://tianchi.aliyun.com/competition/entrance/532486) + +1. 在「赛题与数据」栏目下载 `public_dataset_a.zip`（约 274MB） + +1. 将压缩包放到项目根目录的 `data/` 下 + +**第二步：运行解析脚本** + +``` +cd /home/ke/projects/afac2026-financial-qa +python -m src.preprocess.prepare_data +``` + +该脚本会自动完成两件事： + +1. 解压 `data/public_dataset_a.zip` 到 `data/raw_dataset/`，得到题目 JSON 和原始 PDF + +1. 调用 `pymupdf4llm` 把 86 份 PDF 逐页转成 Markdown，输出到 `data/processed_pymupdf4llm/` + +解析后的目录结构： + +``` +data/ +├── public_dataset_a.zip # 赛方原始压缩包 +├── raw_dataset/ # 解压后的原始数据 +│ ├── questions/group_a/ # A 组 100 道题目 JSON +│ └── raw/ # 86 份原始 PDF +└── processed_pymupdf4llm/ # 解析产物（每页一个 page_XXXX.md） +├── insurance/1/page_0001.md +├── regulatory/1/page_0001.md +└── ... +``` + +每篇文档被切分为多个 `page_XXXX.md`，按文件名排序后拼接即为完整文本。`doc_id`（如 `1`、`2`）与题目 JSON 中的 `doc_ids` 一一对应。 + +### 3.4 配置 API Key + +**获取 API Key** + +![](https://cdn.nlark.com/yuque/0/2026/png/69403934/1781683356318-b43ca47d-430b-4641-9b98-e9a09b6c6788.png) + +1. 登录阿里云百炼控制台：[https://bailian.console.aliyun.com/](https://bailian.console.aliyun.com/) + +1. 进入左侧导航栏 **API Key** 页面（或直接访问 [https://bailian.console.aliyun.com/?tab=model#/api-key）](https://bailian.console.aliyun.com/?tab=model#/api-key）) + +1. 单击 **创建 API Key**，选择默认业务空间，权限选 **全部** + +1. 创建成功后立即复制并保存完整的 Key（关闭弹窗后无法再次查看明文） + +![](https://cdn.nlark.com/yuque/0/2026/png/69403934/1781683356118-c65e3bf4-f104-4e4a-acc8-efd8466100e7.png) + +新用户有 100 万 Token 免费额度，有效期 90 天。 + +**配置到项目** + +在 `.env` 文件中写入： + +``` +DASHSCOPE_API_KEY=sk-your-key-here +``` + +本 baseline 使用阿里云百炼平台（DashScope）的 Qwen 系列模型，接口兼容 OpenAI SDK。详细接入说明见 `design-draft/general/reference/阿里云百炼-Qwen-API-接入笔记.md`。 + +**接入其他模型（实验用途）** + +初赛阶段允许使用其他模型做实验。代码底层通过 OpenAI SDK 调用，只要 API 兼容 OpenAI 格式就能接入。修改方式有两种： + +**方式一：通过 .env 文件（推荐）** + +``` +DASHSCOPE_API_KEY=sk-your-key-here + +# 切换模型（可选，默认 qwen-plus） +# MODEL_NAME=qwen-turbo + +# 切换平台（实验用途，正式提交必须回退到百炼） +# API_BASE_URL=https://api.openai.com/v1 +``` + +代码读取优先级：**环境变量 > config.yaml > 默认值**。也就是说，如果你在 .env 中设置了 `MODEL_NAME` 和 `API_BASE_URL`，代码会优先使用环境变量；如果没设置，回退到 config.yaml；如果 config.yaml 也没改，使用默认值（百炼平台 + qwen-plus）。 + +**方式二：通过 config.yaml** + +``` +model: +name: "gpt-4o" # 或 deepseek-chat、claude-3-sonnet 等 +api_base: "https://api.openai.com/v1" +``` + +注意：**正式提交必须使用 Qwen 系列模型**，其他模型仅供实验调试用。 + +### 3.5 快速运行（5 分钟上手） + +配置完成后，运行以下命令即可得到第一个可提交的 `submission.csv`： + +``` +cd /home/ke/projects/afac2026-financial-qa +python -m src.agent.run --split A --limit 5 --workers 4 +``` + +参数说明： + +- `--split A`：运行 A 组题目 + +- `--limit 5`：只处理前 5 道题（适合快速验证），其余 95 题自动按题型规则随机填充 + +- `--workers 4`：并发线程数 + +运行结束后在 `output/` 目录下生成： + +- `results_a.json`：每道题的 qid、answer、Token 消耗明细 + +- `submission_a.csv`：符合赛题提交要求的 CSV 文件 + +**预期表现**：5 道题约 1-2 分钟跑完。如果这步跑通了，说明环境和 API Key 都配置正确，可以继续往下阅读核心模块的原理。 + +## 4 项目结构 + +``` +src/ +├── agent/ +│ ├── agent.py # Agent 核心逻辑 +│ └── run.py # CLI 入口（并发执行） +├── evaluation/ +│ └── evaluator.py # 答案评测 + 提交文件生成 +├── utils/ +│ ├── llm_client.py # 双渠道 LLM 客户端 +│ └── helpers.py # 工具函数（配置加载、答案规范化等） +└── __init__.py +``` + +总代码量约 900 行，全部在 `src/` 目录下，没有外部索引依赖，纯 Python 实现。 + +## 5 核心模块详解 + +如果你已经通过 **4.5 快速运行** 跑通了 baseline，本节将详细介绍每个模块的设计思路和代码实现。如果你还没跑通，建议先回到 4.5 完成上手验证，再阅读本节。 + +### 5.1 数据层 + +#### 题目加载 + +`run.py` 中的 `load_questions()` 遍历 `*_questions.json`，按 `split` 字段过滤出 A 组或 B 组题目： + +``` +def load_questions(questions_dir: Path, split: str) -> list: +questions = [] +for domain_file in sorted(questions_dir.glob("*_questions.json")): +data = load_json(domain_file) +for q in data: +if q.get("split", "").upper() == split.upper(): +questions.append(q) +questions.sort(key=lambda x: x["qid"]) +return questions +``` + +单条题目结构： + +``` +{ +"qid": "ins_a_001", +"domain": "insurance", +"split": "A", +"question": "关于四个养老保险...", +"options": {"A": "...", "B": "...", "C": "...", "D": "..."}, +"answer_format": "mcq", +"doc_ids": ["1", "2", "15", "16"] +} +``` + +#### 文档读取 + +`FinancialQAAgent._read_document()` 按 `domain/doc_id/` 路径查找 `page_*.md`，排序后拼接： + +``` +def _read_document(self, domain: str, doc_id: str) -> str: +for root in [dataset_dir / "processed_pymupdf4llm", ...]: +parsed_dir = root / domain / str(doc_id) +if parsed_dir.exists(): +pages = sorted(parsed_dir.glob("page_*.md")) +return "\n\n".join(p.read_text(encoding="utf-8") for p in pages) +return f"[文档 {doc_id} 未找到]" +``` + +这里做了路径容错：优先从 `config.yaml` 指定的路径查找，回退到相对路径 `data/processed_pymupdf4llm`。 + +### 5.2 Agent 核心 + +#### ContextManager：简单截断 + +``` +class ContextManager: +def __init__(self, max_chars: int = 320000, max_doc_chars: int = 4000): +self.max_chars = max_chars # 总 prompt 上限 +self.max_doc_chars = max_doc_chars # 单篇文档上限 + +def truncate_doc(self, text: str) -> str: +if len(text) <= self.max_doc_chars: +return text +return text[:self.max_doc_chars] + "\n\n[文档后续内容已省略]" +``` + +`max_doc_chars=4000` 是一个经验值。保险/监管文档通常 5000-8000 字符，截断到 4000 基本能覆盖核心条款；财报/研报动辄 10 万字符以上，4000 字符只能覆盖目录和前言，这是本 baseline 最大的短板。 + +#### PromptBuilder：固定模板 + +``` +class PromptBuilder: +def build_prompt(self, question, evidence, context_manager): +# 每篇文档单独截断后拼接 +context_parts = [] +for ev in evidence: +content = context_manager.truncate_doc(ev.content) +context_parts.append(f"【文档 {ev.doc_id}】\n{content}") +context = "\n\n".join(context_parts) + +options_text = "\n".join( +[f"{k}. {v}" for k, v in question.get("options", {}).items()] +) + +prompt = ( +"你是一位金融文档分析专家。请根据以下提供的文档内容，回答问题。\n" +"要求：\n" +"1. 仔细阅读文档中的相关条款、数据和事实\n" +"2. 对每个选项进行独立判断\n" +"3. 只输出最终答案字母，不要解释过程\n" +"4. 多选题答案按字母顺序排列，不加分隔符\n\n" +f"{context}\n\n" +f"问题：{question['question']}\n\n" +f"选项：\n{options_text}\n\n" +f"答案：" +) +return prompt +``` + +这个 prompt 的设计思路： + +- 明确角色（金融文档分析专家），让模型进入"严肃阅读"模式 + +- 要求"只输出字母"，减少后处理难度 + +- 多选题要求"按字母顺序排列"，避免 "AC" 和 "CA" 被判定为不同答案 + +#### AnswerValidator：答案格式校验 + +``` +class AnswerValidator: +@staticmethod +def validate_mcq(answer: str) -> bool: +return answer in {"A", "B", "C", "D"} + +@staticmethod +def validate_multi(answer: str) -> bool: +return all(c in "ABCD" for c in answer) and answer == "".join(sorted(answer)) and answer != "" + +@staticmethod +def validate_tf(answer: str) -> bool: +return answer in {"A", "B"} +``` + +校验规则直接对应赛题三种题型。`multi` 要求字母去重排序，所以 "ABBC" 会被规范化为 "ABC"。 + +#### FinancialQAAgent：主流程编排 + +``` +def answer_question(self, question) -> Tuple[str, List[Evidence], Dict]: +# 1. 加载证据（读取 doc_ids 对应的文档） +evidence = self._load_evidence(question) + +# 2. 构建 prompt（截断 + 拼接） +prompt = self.prompt_builder.build_prompt(question, evidence, self.context_manager) +prompt = self.context_manager.truncate(prompt) + +# 3. 调用 LLM +messages = [{"role": "user", "content": prompt}] +response = self.llm.chat(messages, max_tokens=4096) + +# 4. 重试机制：内容被过滤或输出为空时，缩短文档再试 +retry_count = 0 +while (response.finish_reason in ("content_filter", "length") +or not response.content.strip()) and retry_count < 2: +self.context_manager.max_doc_chars //= 2 +... # 重新构建 prompt 并调用 + +# 5. 解析答案 +answer = self._parse_answer(response, question.get("answer_format", "mcq")) +return answer, evidence, token_usage +``` + +主流程非常直接：读文档、拼 prompt、问模型、取答案。重试机制是本方案唯一的"鲁棒性"设计：当模型因内容过长触发过滤或输出为空时，自动将单篇截断长度减半（4000 -> 2000 -> 1000），最多重试 2 次。 + +### 5.3 LLM 调用层 + +`llm_client.py` 封装了**双渠道调用策略**（均在百炼平台内）： + +``` +class LLMClient: +def __init__(self, api_key, base_url, model, temperature): +# 主渠道：qwen-plus（性价比高，文档分析能力强） +self.router_client = OpenAI( +api_key=api_key, +base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", +) +self.router_model = "qwen-plus" + +# 回退渠道：qwen-turbo（同一平台内的备用模型） +self.fallback_client = OpenAI( +api_key=api_key, +base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", +) +self.fallback_model = "qwen-turbo" +``` + +触发回退的条件： + +- HTTP 429（限流） + +- HTTP 5xx（服务端错误） + +- 网络连接超时 + +双渠道策略本质上是一种**冗余/容错设计**。主模型因限流或服务端异常不可用时，自动切换到备用模型继续服务。这种设计在跨服务商场景下价值更大（不同服务商的审查机制、限流策略、可用性互相独立），但本次比赛限定使用阿里云百炼，所以回退只是同一平台内不同模型之间的切换。代码里保留这个结构是为了展示"回退设计"这一工程思维，方便读者学习。实际跑 A 组 100 题时，约 5-10% 的题目会触发回退。 + +**注意：** 百炼平台的 Qwen 系列模型支持 `enable_thinking` 参数（通过 `extra_body` 传入），用于控制是否开启思考模式。金融选择题不是复杂推理任务，开启思考会额外消耗大量 completion token 且对准确率帮助有限，因此代码中显式设置为 `False` 关闭。`reasoning_effort` 是 DeepSeek 系列特有的参数，Qwen 不支持。 + +相关文档： + +- 深度思考模型用法（含 `enable_thinking` 参数说明）：[https://help.aliyun.com/zh/model-studio/deep-thinking](https://help.aliyun.com/zh/model-studio/deep-thinking) + +- DashScope API 参考：[https://help.aliyun.com/zh/model-studio/qwen-api-via-dashscope](https://help.aliyun.com/zh/model-studio/qwen-api-via-dashscope) + +### 5.4 评测与提交层 + +`evaluator.py` 包含两部分： + +**Evaluator**：如果有标准答案，可以计算准确率和 Token 效率分。 + +``` +def compute_final_score(self, accuracy: float, total_tokens: int) -> float: +token_score = max(0.0, min(1.0, (self.token_budget - total_tokens) / self.token_budget)) +return 100.0 * accuracy * (0.7 + 0.3 * token_score) +``` + +公式含义：准确率占 70% 权重，Token 效率占 30% 权重。在 500 万 Token 预算内，消耗越少得分越高。 + +**SubmissionGenerator**：生成赛题要求的提交文件。 + +``` +def generate_csv(self, answers, token_stats) -> Path: +with open(path, "w", newline="", encoding="utf-8") as f: +writer = csv.writer(f) +writer.writerow(["qid", "answer", "prompt_tokens", "completion_tokens", "total_tokens"]) +# 第一行是汇总 +writer.writerow(["summary", "", total_prompt, total_completion, total_tokens]) +# 随后每行一题 +for qid in sorted(answers.keys()): +... +``` + +CSV 第一行必须是 `summary`，汇总全量 Token 消耗，否则评测脚本会报错。 + +## 6 运行方式 + +### 6.1 命令行运行 + +``` +cd /home/ke/projects/afac2026-financial-qa +python -m src.agent.run --split A --workers 8 +``` + +参数说明： + +- `--split A`：运行 A 组题目 + +- `--workers 8`：并发线程数（百炼 API 并发限制约 10，留 2 个余量） + +- `--output results_a.json`：可选，自定义输出路径 + +- `--limit 10`：可选，只处理前 10 道题，其余随机填充。适合快速验证流程或测试 API 连通性 + +**快速测试示例（只跑 5 题，约 1-2 分钟）：** + +``` +python -m src.agent.run --split A --limit 5 --workers 4 +``` + +未处理的 95 题会自动填充随机答案（单选/多选/判断分别按题型规则生成），生成的 `submission_a.csv` 格式完整，可直接提交到评测系统查看格式是否通过。 + +### 6.2 输出文件 + +运行结束后在 `output/` 目录下生成： + +- `results_a.json`：每道题的 qid、answer、各阶段 Token 消耗 + +- `submission_a.csv`：符合赛题提交要求的 CSV 文件 + +### 6.3 预期表现 + +- 成功率：100%（100 题全部有输出） + +- Token 消耗：约 36-40 万 prompt tokens + 10-15 万 completion tokens + +- 准确率：未知（没有标准答案库时无法计算） + +- 答案分布：A 选项偏多（约 36/100），可能存在位置偏差 + +## 7 局限与优化路线图 + +本 baseline 的每一个局限都对应一条清晰的优化路径。但在动手优化前，需要建立一个正确预期：**本次赛题的准确率并不容易提升。** + +原因有三： + +1. **题型对模型综合能力要求高**。一道题可能同时涉及跨文档阅读（4 份条款）、公式计算（提取参数后代入）、逻辑推理（判断例外情形是否适用）和精细比较（排序或判断正误）。这意味着模型不仅要"读到"信息，还要"算对""想通""比准"。 + +1. **金融知识本身门槛高**。保险条款中的"等待期""免赔额""责任免除"，监管法规中的"受益所有人识别""客户尽职调查"，这些概念对非专业人士来说理解成本很高。模型如果没有足够的金融领域预训练知识，光凭 prompt 很难做到精准理解。 + +1. **优化方案往往有副作用**。比如引入检索可以提升长文档覆盖率，但可能漏掉关键否定词；让模型做详细推理可以提升判断准确性，但会大幅增加 Token 消耗。每改一个地方，可能解决一个问题，又引入另一个问题。 + +因此，不要期望某个单一技巧就能让准确率大幅提升。真实的提分路径是**多模块协同优化**：文档解析质量 + 检索质量 + 上下文管理（动态记忆压缩）+ prompt 设计 + 答案检查，层层叠加才能看到效果。 + +**与官方优化方向的对照** + +赛题官方在 baseline 说明中明确列出了五个重点优化方向，和我们的思路基本一致： + +| 官方方向 | 对应本教程的模块 | + +| --- | --- | + +| 文档解析质量：提升 PDF 文本抽取、表格处理和版面结构还原能力 | **8.1 PDF 解析质量** | + +| 检索策略：构建面向金融术语、条款编号和指标名称的混合检索 | **8.2 上下文管理粗放**（含检索层） | + +| 证据聚合：支持同题多文档、多段落证据的联合判断 | **8.2 上下文管理**（片段组装） | + +| 答案约束：对模型输出进行字母抽取、排序、去重和合法性检查 | **8.3 单轮推理无自检、答案检查薄弱** | + +| 领域知识增强：针对保险、监管、合同、财报、研报分别设计提示词 | **8.4 Prompt 简陋**（领域特化）+ **8.5 无结构化知识提取** | + +其中最容易被忽略的是**文档解析质量**。如果 PDF 解析阶段就把表格拆散了、数字漏掉了、段落顺序搞错了，后续无论检索多精准、推理多严密，都是建立在错误信息上的。赛题官方也明确将"提升 PDF 文本抽取、表格处理和版面结构还原能力"列为重点优化方向之一。 + +### 7.1 PDF 解析质量 + +本 baseline 使用 pymupdf4llm 将 PDF 转为 Markdown，解析质量直接决定了后续所有环节的上限。如果解析阶段出现以下问题，准确率再高也救不回来： + +- **表格丢失或错位**：财报中的三大报表如果行列对应关系被破坏，模型读到的"营业收入"和"净利润"可能是同一列的数据，计算题直接全军覆没 + +- **数字截断或变形**：条款中的金额、比例、年限等关键数字如果解析出错（比如"100,000"变成"100"），公式计算的结果必然错误 + +- **段落顺序错乱**：保险条款中"等待期"的定义和"例外情形"如果不在同一段落附近，模型可能只看到前半句"等待期内不赔"，漏掉后半句"因意外伤害除外" + +- **标题层级丢失**：监管法规的"第一章 总则""第二章 适用范围"等层级结构如果被打平成纯文本，模型难以判断条款的适用优先级 + +**优化方向**： + +- **多解析器对比**：同一篇 PDF 用 pymupdf4llm、Marker、MinerU 等多种工具解析，取质量最好或互补性最强的一份 + +- **表格专项处理**：对财报/合同中的表格使用专门的表格解析工具（如 camelot、tabula），保留行列结构，转成 Markdown 表格或 CSV + +- **版面结构还原**：在 Markdown 中保留页码、章节标题、条款编号等结构信息（如 `# 第一章 保险责任`、`**第 3.2 条 等待期**`），帮助模型快速定位 + +- **解析后校验**：用规则或 LLM 检查解析结果是否存在明显异常（如连续 20 行都是乱码、表格列数不一致、金额数字明显缺失），异常时切换备用解析器 + +需要特别注意的是：**PDF 解析是一次性投入**。A 榜 86 份 PDF 解析一次后可以复用，不受 500 万 Token 总预算限制。在解析阶段投入精力换取更高质量的输入文本，性价比远高于在答题阶段用更多 Token 去弥补解析错误。 + +### 7.2 上下文管理粗放（文档截断粗暴） + +本 baseline 保留文档前 4000 字符，对于财报/研报/合同来说命中关键信息的概率很低。这是上下文管理问题的一个具体表现：系统对"保留什么、丢弃什么、怎么拼接"没有任何策略，只是机械地截断。优化方向分两层： + +**（1）文本切片 + BM25** + +先把文档切成固定长度或按段落切分，再用 BM25 算法计算每个片段与问题的相关性得分，取 top K 送给 LLM。优点是轻量、不依赖外部模型，jieba 分词 + rank-bm25 库就能跑通；缺点是只考虑词频匹配，无法理解语义（比如"退保费用"和"退保手续费"会被当成两个词）。 + +**（2）无索引关键词匹配 + 滚动窗口** + +不建索引，直接在文档中搜索问题关键词，提取关键词所在位置的前后若干字符作为上下文。复现门槛最低，但召回质量波动大。 + +对应实现方式： + +- **BM25 单次检索 top 5**：用 jieba 分词、按领域差异化 chunk_size，轻量且可控 + +- **无索引关键词匹配 + 滚动窗口**：复现门槛更低，不依赖预建索引 + +**上下文管理的更高层次：片段组装与动态压缩** + +即使有了检索，如何把搜到的片段拼成一份高质量的 prompt，仍然是上下文管理的核心问题： + +- **片段去重与边界修复**：检索返回的 top K 片段可能存在重叠（比如 chunk1 结尾和 chunk2 开头重复了 200 字），直接拼接会造成内容冗余，浪费 Token。需要去重并保证段落边界完整。 + +- **信息密度排序**：检索结果不是全部值得放入 prompt。按相关性得分排序后，设定一个阈值，只保留得分明显高于阈值的片段，把有限的上下文窗口留给真正相关的信息。 + +- **多轮上下文传递**：迭代检索时，第一轮搜到的关键信息要在第二轮 prompt 中保留，避免"搜到了又忘了"。但同时要控制历史信息的累积长度，防止 prompt 无限膨胀。 + +- **按题型动态调整上下文长度**：简单的事实查找题可能只需要 2000 字的上下文；跨文档对比题可能需要 4 篇文档各取 top 3 片段，总共上万字；计算题则需要确保公式所在的段落不被截断。上下文长度不应是固定值，而应根据题目复杂度自适应调整。 + +### 7.3 单轮推理无自检、答案检查薄弱 + +本 baseline 只做单轮调用，模型给出答案后不做二次核验，对错全凭一次输出。而且答案检查仅限于格式校验（是不是合法字母），不做内容层面的合理性判断。优化方向分两层： + +**（1）推理层：从单轮到多轮** + +- **迭代检索**：LLM 判断信息是否充分，不够则生成新查询继续搜 + +- **自我纠错**：让模型回顾自己的答案，检查是否与文档证据矛盾。比如模型选了 A，但文档明确说"以下情形不属于保险责任"，自我纠错环节可以发现这个冲突 + +- **多选题逐项验证**：多选题让每个选项单独过一遍"文档是否支持"，比一次性让模型选所有答案更不容易漏选 + +**（2）答案检查层：从格式校验到内容校验** + +当前 baseline 的 `AnswerValidator` 只检查答案格式（mcq 是不是单个字母、multi 字母是否排序），这是**最底层**的检查。更高层的检查包括： + +- **证据一致性校验**：模型说答案是 A，但检索到的证据片段明确支持 B，系统应该能识别这个矛盾。实现方式可以是让模型在输出答案的同时给出判断依据，然后用另一轮调用验证"依据是否支持答案" + +- **题型规则校验**：判断题只有 A/B 两个选项，如果模型输出 C，直接判定为异常并 fallback；多选题至少要有两个正确选项（按出题习惯），如果模型只选了一个，可以触发二次确认 + +- **多轮一致性校验**：同一道题用相同 prompt 调用两次，如果两次答案不同，说明模型对该题不确定，可以引入第三次调用取多数，或改为保守策略（如多选题在有争议时少选） + +- **位置偏差修正**：如果发现某次提交的答案 A 明显偏多（如 40% 以上），可以对 A 选项的题增加二次确认，或在 prompt 中加入"不要偏向第一个选项"的去偏指令 + +- **默认值策略**：模型输出为空或无法解析时，按题型规则 fallback。单选默认 A，判断默认 A，多选默认 AB（保守策略是选最少的可能组合） + +### 7.4 Prompt 简陋 + +本 baseline 用同一套 prompt 处理所有题型和领域。优化方向： + +- **混合策略**：短文档全文读取、长文档检索，按文档长度动态选择策略 + +- **领域特化**：针对不同领域和题型设计专用提示词，加入去偏指令（"不要偏向第一个选项"） + +### 7.5 无结构化知识提取 + +本 baseline 把文档当纯文本处理，模型每次回答都要重新阅读原始段落。如果题目要求比较 4 份保险条款的等待期、免赔额、退保费用，模型需要在 4 份文档中反复搜索同类信息，效率低且容易遗漏。 + +**优化方向：构建领域知识图谱** + +知识图谱的核心价值在于把分散在各文档中的结构化信息抽取出来，统一存储为"实体-关系-属性"三元组。例如从保险条款中提取： + +``` +(国寿增益宝, 等待期, 30天) +(国寿增益宝, 免赔额, 10000元) +(国寿增益宝, 第7年退保费用, 0%) +(平安智盈金生, 等待期, 90天) +... +``` + +当题目问"以下哪款产品的等待期最短"时，系统不需要再去读原始文档，直接从图谱中按"等待期"属性排序即可。这种方式在跨文档聚合类题目上优势明显：信息已经结构化，比较和推理的准确率远高于让模型在原始文本中自行定位。 + +具体实现可以分两步： + +1. **离线抽取**：预处理阶段用 LLM 遍历所有文档，按领域定义的信息模板（保险条款提取"等待期/免赔额/退保费用/身故保险金"，财报提取"营收/净利润/研发投入/现金流"）抽取结构化字段，存入 JSON 或图数据库 + +1. **在线查询**：答题时先判断题目是否涉及结构化比较，如果是则优先查知识图谱；如果是需要原文引用的条款解释题，再回退到原始文本检索 + +知识图谱的代价是预处理阶段需要消耗额外 Token 做信息抽取，但在答题阶段可以大幅减少重复阅读，整体 Token 效率可能更优。而且如果允许的话，离线抽取不受 500 万 Token 总预算限制，性价比很高。不过赛题方是有对知识图谱构建的过程计算对应的token的，是否能满足我们的实际要求的话，还需要使用实战来进行测试了。 + +## 附录 A：核心参数速查 + +| 参数 | 默认值 | 说明 | + +| --- | --- | --- | + +| `max_doc_chars` | 4000 | 单篇文档截断长度 | + +| `max_chars` | 320000 | 总 prompt 截断上限 | + +| `max_tokens` | 4096 | LLM 最大输出长度 | + +| `temperature` | 0.0 | 采样温度（greedy） | + +| `workers` | 8 | 并发线程数 | + +| `enable_thinking` | False | 关闭思考模式 | + +本教程对应的源码位于项目根目录 `src/` 下。进阶优化方向、题型分析和金融文档背景知识请参考进阶教程。 + +## 参考文献 + +[1] PyMuPDF4LLM: Convert PDF to Markdown via PyMuPDF. Artifex Software, 2024. GitHub: [pymupdf/PyMuPDF4LLM](https://github.com/pymupdf/PyMuPDF4LLM) + +[2] AFAC2026 赛道四：金融长文本 Agent 的动态记忆压缩与高效问答。赛方文档，2026。参见 `docs/赛题与数据.md`、`docs/赛事介绍.md` + +[3] 阿里云百炼 DashScope API 参考。参见 [https://help.aliyun.com/zh/model-studio/](https://help.aliyun.com/zh/model-studio/) + +[4] BM25 检索算法。Robertson & Walker, 1994. Okapi at TREC-3. 以及 Trotman, Puurula & Burgess, 2014. Improvements to BM25 and Language Models Examined. arXiv:1401.4737 + + diff --git a/reference/baseline.md b/reference/baseline.md new file mode 100644 index 0000000..2d3649f --- /dev/null +++ b/reference/baseline.md @@ -0,0 +1,1797 @@ +本文档面向已经跑通 baseline 流程、希望对系统做进一步优化的参赛者。建议先阅读 Baseline 教程 再回来。 + +赛事官方地址：[https://tianchi.aliyun.com/competition/entrance/532486](https://tianchi.aliyun.com/competition/entrance/532486) + +## 1 赛题题目类型分析 + +A 组 100 道题看似都是"读文档选答案"，实际按解题方法可以分成几类。理解这些类型，才能明白为什么"搜索到片段直接回答"远远不够。 + +### 1.1 公式计算题 + +题目给出具体数值，要求从条款中提取计算公式后代入计算。 + +**示例：ins_a_002（退保金排序）** + +平安智盈金生累计所交保费 10 万元，保单账户累计收益 2 万元，在第 7 个保单年度末退保；国寿增益宝个人账户价值 12 万元，在第 7 个保单年度末退保（退保费用 0%）；平安富鸿金生现金价值 9 万元。则三者退保所得金额从高到低排序为？ + +这道题需要从 3 份保险条款中分别找到退保金的计算公式： + +- 平安智盈金生：第 6-10 年现金价值 = 累计保费 + 收益 × 75% → 10 + 2 × 0.75 = 11.5 万 + +- 国寿增益宝：退保费用 0%，退保金 = 个人账户价值 = 12 万 + +- 平安富鸿金生：现金价值直接给出 = 9 万 + +排序：12 > 11.5 > 9 → 选项 A 正确。 + +这类题的难点是**公式分散在不同文档**，必须逐份定位条款，提取参数，再做算术。只搜"退保"两个字得到的片段大概率漏掉"第 6-10 年"这个关键条件。 + +**示例：ins_a_003（多保险联合赔付计算）** + +李某因白血病复发住院，总费用 8 万元，医保报销 3 万元，自费 5 万元。众安白血病医疗险免赔额为 0；平安 e 生保（计划二，免赔额 5000 元）；太保团体百万医疗（免赔额 1 万元）。三家保险公司共应赔付多少？ + +这道题涉及 3 份医疗险条款，每份的赔付规则不同：有的先扣免赔额再按比例赔，有的 0 免赔全额赔，还要看费用是否属于保险责任。最终答案需要通过**多条款交叉计算**得出，不是单文档搜索能解决的。 + +### 1.2 跨文档对比题 + +题目明确要求对比两份或多份文档中的数据或条款。 + +**示例：fin_a_001（比亚迪连续两年年报对比）** + +根据比亚迪连续两年的年度报告，下列关于公司经营业绩变化的描述中，哪些是准确的？ A. 2025 年营业收入较 2024 年实现增长 B. 2025 年归属于上市公司股东的净利润出现下滑 ... + +需要分别从 2024 年报和 2025 年报中提取营业收入、净利润、经营现金流、研发投入等数据，再逐项对比增长/下滑关系。单看任意一份年报都不够。 + +**示例：fin_a_005（跨公司财务指标对比）** + +基于比亚迪与美的集团 2024 年年度报告的数据对比，关于两家企业关键财务指标的结论，哪些是成立的？ + +这道题需要同时打开比亚迪和美的两份年报，提取同一类指标（营业收入规模、研发投入强度、经营现金流等）做横向比较。如果检索模块只返回了比亚迪的数据，美的的数据没召回，答案就直接错一半。 + +### 1.3 多文档联合推理题 + +题目涉及多个产品/法规/条款，需要把各文档的信息拼起来做综合判断。 + +**示例：ins_a_001（四产品身故保险金排序）** + +关于四个养老保险/寿险产品的"身故保险金"，以下计算结果正确的是？假设所有产品的已交保费均为 100 万元...平安智盈金生（领取日前）保单账户价值 90 万元；国寿增益宝（一人，40 岁）基本保额 90 万元，个人账户价值 85 万元... + +涉及 4 份保险条款，每份的身故保险金计算公式都不一样：有的是"已交保费 vs 现金价值取大"，有的是"已交保费 × 比例 + 账户价值"，有的是"基本保额 × 系数"。必须四份条款都读到、都算对，才能排序。 + +**示例：ins_a_005（多产品免责范围判断）** + +下列哪些情形属于相应保险产品的免责范围？ A. 平安智盈金生：被保险人因酒后驾驶导致失能失智护理状态 B. 平安安佑福重疾险：被保险人因感染艾滋病病毒（非输血、职业、器官移植）导致重大疾病 ... + +4 个选项分别来自 4 份不同条款，每个选项都要到对应条款里找"责任免除"章节，确认该情形是否在免责列表中。这本质上是**4 次独立的条款定位任务**，拼在一起成了一道多选题。 + +### 1.4 条款定位题 + +题目问的是某一条款的具体规定，答案就在文档的某个章节里。 + +**示例：ins_a_004（等待期内出险处理）** + +关于"等待期"内出险的处理，以下哪种情形下保险公司需要承担赔偿责任？ A. 投保平安安佑福重疾险第 50 天，因意外车祸导致双目失明（符合重大疾病） ... + +4 个选项涉及 4 份不同保险，每份的"等待期"条款可能都有例外规定（比如"因意外伤害发生上述情形的，无等待期"）。需要逐份定位等待期条款，再判断该选项情形是否适用例外。 + +### 1.5 事实查找题 + +题目问的是文档中直接记载的事实，不需要计算，只需要定位。 + +**示例：res_a_001（行业数据查找）** + +关于银保渠道发展历程及银行 IT 市场规模的描述，下列哪些选项是正确的？ A. 韩国寿险银保渠道保费贡献率在 2022 年达到了 56% B. 2005 年至 2018 年期间，银保渠道实现复合增速 9.9% ... + +这类题在研报中通常有明确的数字和结论，但研报动辄几万字，数字散落在不同段落。如果检索只返回了前半部分，可能恰好漏掉了"2022 年"这个限定词。 + +### 小结 + +这五种类型不是互斥的，很多题同时涉及多种能力。比如 ins_a_003 既是**多文档联合推理**（3 份条款），又是**公式计算**（每份条款的赔付公式不同）。fin_a_001 既是**跨文档对比**（两年年报），又是**事实查找**（具体数值）。 + +这意味着一个有效的系统至少要解决两个问题： + +1. **如何确保所有相关文档都被读到**（跨文档题不能只读一篇） + +1. **如何在文档中精确定位关键信息**（计算题要找到公式，条款题要找到对应章节） + +硬截断策略对第 1 个问题无能为力（4 篇文档每篇截断 4000 字，总长度可能超限），对第 2 个问题取决于关键信息是否恰好在前 4000 字内。后续优化方向，本质上都是在解决这两个问题。 + +### 1.6 什么是 RAG（检索增强生成） + +上面两个问题的标准解决方案，在 NLP 领域有一个专门的名字：**RAG（Retrieval-Augmented Generation，检索增强生成）**。它的核心思想很简单：**在让模型回答之前，先从文档中把相关内容找出来，只把最相关的片段交给模型，而不是把整篇文档都塞过去。** + +标准的 RAG 流程分三步： + +**（1）索引（Indexing）** + +把所有文档切成小块（chunk），每块几百到几千字符，然后用 Embedding 模型把每块文本编码成向量，存入向量数据库。这样每段文本都有了一个"地址"，可以通过向量相似度快速定位。 + +**（2）检索（Retrieval）** + +收到用户问题后，把问题也编码成向量，到向量数据库里搜索最相似的 top K 个文本块。这一步的本质是"找证据"，即从海量文档中挑出可能包含答案的那几段。 + +**（3）生成（Generation）** + +把检索到的文本块和原始问题一起拼成 prompt，交给 LLM 生成最终答案。因为 LLM 看到的不是整篇文档，而是经过筛选的最相关片段，所以既减少了噪音，又降低了 Token 消耗。 + +**RAG 与本赛题的关系** + +金融长文本问答本质上就是一个 RAG 任务：给定一个问题（如"平安智盈金生的退保金怎么算"），系统需要从指定文档中**检索**到相关条款，然后**生成**正确答案。本方案的硬截断等于跳过了"检索"这一步，直接把文档前 4000 字全部塞给模型，让模型自己从噪音里找信号。后续优化的核心，就是逐步补齐 RAG 的各个环节：更好的分块策略、更准的检索算法、更合理的片段组装方式。 + +### 1.7 典型题目深度解析：reg_a_014 + +为了更直观地理解赛题的难度和解题方法，下面以一道官方提供的典型题目为例，展示完整的证据定位、逻辑推理和答案推导过程。 + +**题目概况** + +| 字段 | 内容 | + +| --- | --- | + +| qid | reg_a_014 | + +| 领域 | regulatory（监管法规） | + +| 题型 | 多选题（multi） | + +| 关联文档 | strict_csrc_023《上市公司治理准则》、strict_csrc_035《上市公司章程指引》 | + +**题干**： + +某 A 股上市公司（非金融类，总股本 5 亿股，最近一期经审计净资产 120 亿元）拟进行四项决策： + +① 为资产负债率为 75% 的全资子公司提供 8 亿元担保； ② 将原募投项目"智能工厂建设"剩余募集资金 6.5 亿元变更为"新能源材料研发中心"； ③ 选举新一届独立董事，其中 1 名候选人系某律所合伙人，其所在律所过去 12 个月内曾为公司提供常年法律顾问服务但未承办重大诉讼； ④ 审议《关于修订公司章程第 42 条股东大会特别决议事项的议案》，拟将"对外提供财务资助"纳入须经特别决议的范围（原章程未列明）。 + +**选项**： + +- **A**：事项①中，因被担保方资产负债率超过 70%，且担保金额占公司最近一期经审计净资产比例达 6.67%，两项任一条件触发即须经股东大会审议通过，故该项担保必须提交股东大会审议。 + +- **B**：事项②中，变更募集资金用途涉及金额 6.5 亿元，虽未达净资产 10%（12 亿元），但因变更投向属于"主营业务方向重大调整"，根据《章程指引》第 44 条及《治理准则》第 19 条，仍须经股东大会特别决议通过。 + +- **C**：事项③中，该独立董事候选人存在《治理准则》规定的"最近 12 个月内为上市公司提供过法律服务"的情形，构成独立性障碍，不得提名为独立董事候选人，故股东大会不得对其行使选举权。 + +- **D**：事项④中，公司章程自主增设特别决议事项不违反《章程指引》第 77 条"上市公司可在本指引基础上增加特别决议事项"的授权性规定，但须经股东大会普通决议通过即可生效，无需特别决议。 + +**标准答案**：AC + +**证据定位与推理过程** + +| 选项 | 关键法条定位 | 推理 | + +| --- | --- | --- | + +| A | 《章程指引》第四十七条第（四）款："为资产负债率超过百分之七十的担保对象提供的担保"须经股东会审议通过 | 被担保方资产负债率 75% > 70%，触发法定审议条件，A 正确 | + +| B | 《章程指引》第八十一条（普通决议事项）、第八十二条（特别决议事项） | 变更募集资金用途属于普通决议事项，未列入特别决议清单，B 错误 | + +| C | 《治理准则》第四十一条：独立董事不得与上市公司存在"直接或间接利害关系" | 候选人所在律所过去 12 个月提供过法律服务，构成利害关系，C 正确 | + +| D | 《章程指引》第八十二条第（三）款："本章程的修改"须经特别决议通过 | 将"对外财务资助"纳入特别决议范围属于修改章程，必须经特别决议，D 错误 | + +**这道题对系统的考验**： + +1. **跨文档定位**：需要同时在《章程指引》和《治理准则》中查找对应条款，不能只读一份文档 + +1. **精确条款匹配**：每个选项的推理都依赖具体条款编号（第四十七条、第八十二条），模型必须定位到精确条款而非泛泛而谈 + +1. **条件判断**：选项 A 中的"资产负债率超过 70%"是一个明确的数值阈值，模型必须准确提取并比较 + +1. **逻辑否定**：选项 B 和 D 都是"看起来合理但实际错误"的干扰项，模型需要识别出"变更募集资金用途是普通决议""修改章程是特别决议"这些否定性结论 + +1. **多选题陷阱**：漏选 C 或多选 B/D 都不得分，必须四个选项全部判断准确 + +这道题的难度在于：它不是"找到原文就能答对"的简单题，而是需要**精准定位多个法条 → 提取关键条件 → 逐项逻辑判断 → 综合得出多选组合**的复杂推理链。如果检索模块漏掉了《章程指引》第八十一条和第八十二条的区分，或者模型把"普通决议"和"特别决议"搞混，答案就直接错了。 + +## 2 PDF 解析方案 + +RAG 系统的质量上限由两个因素决定：检索策略和文档解析质量。Baseline 教程第七章已经指出了 PDF 解析质量是第一个瓶颈，但只给了方向没有展开。这一章详细对比主流方案，并解释 baseline 的选择逻辑。 + +### 2.1 为什么 PDF 解析是第一步且最重要 + +整个 pipeline 的链路是：PDF → 解析 → 文本 → 切片 → 检索 → LLM 回答。如果第一步就把表格变成了乱码、公式丢失了、段落顺序乱了，后面所有环节都在错误的文本上工作。这种错误无法通过优化检索或 prompt 来补救。 + +保险条款和财报类 PDF 有几个让解析器头疼的特点： + +- **复杂表格**：保费表、现金价值表、赔付比例表，行列密集且嵌套层级深 + +- **数学公式**：精算公式、财务比率公式，上下标和分式结构容易丢失 + +- **双栏/多栏布局**：部分研报和合同使用多栏排版，阅读顺序容易错 + +- **页眉页脚混入正文**：页码、章节标题、公司名称等重复内容污染正文 + +- **扫描件 vs 电子件**：部分文档是扫描图片（需要 OCR），部分是原生 PDF（可直接提取文字），两者需要不同的处理策略 + +### 2.2 主流方案对比 + +#### （1）轻量级：PyMuPDF4LLM（当前 baseline 方案） + +**PyMuPDF4LLM** 是基于 PyMuPDF（原 fitz）的轻量封装，专门为 LLM 场景设计。它的核心能力是把 PDF 直接转为 Markdown 文本，保留标题层级（通过字号和字体推断），提取表格为 Markdown table 格式。 + +**优势：** + +- 纯 Python 库，安装简单（`pip install pymupdf4llm`） + +- 不需要 GPU，纯 CPU 运行 + +- 速度快，一份 50 页 PDF 约 5-10 秒完成 + +- 对原生 PDF（非扫描件）的文字提取质量尚可 + +- 输出 Markdown 格式，直接兼容下游处理 + +**局限：** + +- 不包含 OCR 能力，扫描件 PDF 无法处理 + +- 表格提取基于规则，复杂嵌套表格容易错位 + +- 公式识别能力弱，数学公式可能丢失结构 + +- 双栏文档的阅读顺序可能出错 + +- 字号推断标题层级不总是准确（比如正文小标题和普通段落字号相同） + +**为什么 baseline 选它：** 赛方评测环境不提供 GPU，PyMuPDF4LLM 是当时能在 CPU 上快速跑通的方案。17% 的准确率中，有一部分损失确实来自 PDF 解析不够好，但 baseline 的目标是先把端到端流程跑通。 + +#### （2）中量级：Marker + +**Marker**（VikParuchuri, 2024, 持续更新至 2025 年 1.4.0 版本，GitHub: VikParuchuri/marker）[1] 是一个基于 pipeline 架构的 PDF 转 Markdown 工具。它的流水线是：Surya 布局检测 → Surya OCR（可选）→ 文本提取 → Markdown 输出。 + +**核心能力：** + +- 布局检测使用 Surya 模型，能识别标题、正文、表格、公式、图片、页眉页脚等区域 + +- 内置 OCR 能力，支持 90+ 种语言 + +- 对扫描件和原生 PDF 都能处理 + +- 输出格式丰富：Markdown、JSON（含位置信息） + +- 支持 GPU 加速，也能纯 CPU 运行（速度较慢） + +**优势：** + +- 表格和公式处理优于纯规则方案 + +- 多语言支持好，中文文档识别质量不错 + +- 自动过滤页眉页脚 + +- 社区活跃，更新频繁 + +**局限：** + +- 依赖 PyTorch 模型，需要 4-8GB 显存（GPU 模式） + +- CPU 模式下速度较慢，一份 50 页 PDF 可能需要 5-15 分钟 + +- 对极端复杂的表格（多层嵌套、合并单元格）仍有遗漏 + +- 公式识别不支持 LaTeX 输出，公式以图片或文本形式保留 + +**对本赛题的适用性：** 如果赛方提供 GPU 环境，Marker 是一个性价比不错的选择。CPU 模式下虽然慢，但解析在预处理阶段完成，不占用提交时的 Token 预算，时间成本可以接受。 + +#### （3）重量级：MinerU + +**MinerU**（上海 AI Lab / OpenDataLab, 2024 年发布, 持续更新至 2026 年 4 月 3.1.0 版本，GitHub: opendatalab/MinerU）[2] 是目前开源社区最受关注的 PDF 解析工具之一，专为 LLM / RAG / Agent 场景设计。 + +**核心能力：** + +- 完整的版面分析管线：布局检测 → 阅读顺序恢复 → 元素识别 → 结构化输出 + +- 表格和公式提取质量高，公式自动转为 LaTeX + +- 支持 109 种 OCR 语言 + +- 输出 Markdown / JSON / HTML / LaTeX 多种格式 + +- 内置 MCP Server 支持，可直接集成到 Agent 系统 + +- 兼容 LangChain、LlamaIndex、Dify、RAGFlow 等主流 RAG 框架 + +**优势：** + +- 公式转 LaTeX 是本赛题的关键优势——保险条款和财报中的数学公式能完整保留 + +- 阅读顺序恢复能力强，双栏/多栏文档不乱序 + +- 对中文文档优化好（上海 AI Lab 团队中文背景） + +- OmniDocBench 评测中英文文档解析表现领先 [3] + +- 同时支持本地部署、在线 API、CLI 和 Python SDK + +**局限：** + +- GPU 依赖：需要 CUDA 支持的 GPU（推荐 8GB+ 显存），纯 CPU 模式可以运行但非常慢 + +- 安装复杂：依赖较多系统库，首次配置可能需要 30 分钟以上 + +- 内存占用大：处理高分辨率文档时可能占用 10GB+ 内存 + +- 单页处理时间：GPU 下约 3-8 秒/页，CPU 下可能 30-60 秒/页 + +**对本赛题的适用性：** 如果赛方允许使用 GPU 环境，MinerU 是目前的最佳选择。公式 LaTeX 化、表格结构化、阅读顺序恢复这三项能力直接解决了本赛题 PDF 解析的核心痛点。即使赛方只给 CPU 环境，MinerU 也可以跑（只是慢），预处理阶段的时间成本通常可以接受。建议关注赛方最终公布的环境配置。 + +补充说明：Meta 在 2023 年发布的 Nougat 是最早用端到端 Transformer 做 PDF 公式解析的工作之一，MinerU 的公式处理模块 UniMERNet 吸收了其思路并做了改进。Nougat 作为独立工具已不再活跃维护，且专为英文学术论文设计，不适用于本赛题的金融文档，此处不再展开。 + +#### （4）视觉语言模型级：olmOCR + +**olmOCR**（Allen AI, 2025 年 2 月发布, arXiv:2502.18443）[4] 代表了 PDF 解析的新方向：不用传统的 OCR + 版面分析流水线，而是用一个精调的 7B 视觉语言模型（VLM）直接把 PDF 页面转成结构化的纯文本。 + +**核心能力：** + +- 基于 7B VLM，精调在 260,000 页多样化 PDF 上 + +- 支持表格、列表、公式、手写文字的提取 + +- 输出线性化的纯文本，保持自然阅读顺序 + +- 批处理效率高，100 万页仅需 176 美元（使用开源模型时几乎免费） + +- 在 olmOCR-Bench 上表现优于 GPT-4o、Gemini Flash 2 和 Qwen-2.5-VL + +**优势：** + +- 不需要复杂的版面分析管线，一个模型搞定所有页面类型 + +- 对扫描件、手写文档、低质量 PDF 的处理能力远超传统 OCR 方案 + +- 开源的 7B 模型可以在本地部署，不需要调用付费 API + +**局限：** + +- 需要 GPU（7B VLM 推理），CPU 不可用 + +- 中文文档的处理质量未知（训练数据以英文为主） + +- 输出格式为纯文本而非 Markdown，丢失了部分结构信息 + +- 表格和公式以文本形式呈现，不如 LaTeX 精确 + +**对本赛题的适用性：** 如果赛方提供 GPU 环境且中文处理质量过关，olmOCR 是一个值得尝试的方案。但目前中文支持情况不明朗，建议等社区有中文评测结果后再决定。 + +### 2.3 本赛题的 PDF 解析选型建议 + +根据赛方可能提供的环境，分三种情况： + +| 环境 | 推荐方案 | 理由 | + +| --- | --- | --- | + +| 纯 CPU | PyMuPDF4LLM + 预处理优化 | 无 GPU 时的唯一选择。可以在预处理阶段做更多工作来补偿解析质量（如人工修正表格、补充公式描述） | + +| GPU（8GB+） | MinerU | 当前开源方案中综合能力最强，公式/表格/阅读顺序三项核心能力突出 | + +| GPU（16GB+） | MinerU + olmOCR 互补 | MinerU 处理结构化文档，olmOCR 处理扫描件和低质量页面，两路互补 | + +**即使赛方最终只给 CPU 环境，学习 MinerU 和 olmOCR 也是有价值的。** 了解更好的解析方案能帮助理解当前 baseline 的瓶颈在哪里（哪些错误是 PDF 解析导致的，哪些是检索策略导致的，哪些是 prompt 设计导致的）。定位清楚瓶颈，才能精准优化。 + +### 2.4 预处理阶段可以做的工作 + +无论用哪个解析工具，以下预处理手段对提升文本质量有帮助： + +1. **页眉页脚过滤**：用正则匹配重复出现的页码、章节标题、公司名称等，统一去除 + +1. **表格后处理**：对解析出的表格做格式校验（行列数一致、数值列对齐），异常时回退到手动提取 + +1. **公式补标注**：在公式附近插入描述性文本（如"该公式表示：现金价值 = 累计保费 × 75%"），帮助 LLM 理解公式含义 + +1. **段落合并**：同一段落的文本可能被解析成多行，按标点和语义做合并 + +1. **文档结构标注**：为每个段落标注所属章节路径（如"第二章 > 第三节 > 现金价值计算"），增强检索时的上下文信息 + +## 3 RAG 检索优化 + +Baseline 的硬截断方案本质上跳过了检索，直接把文档前半部分塞给模型。要突破 17% 的准确率天花板，第一步就是引入检索模块，但必须注意检索引入的连贯性断裂风险。 + +### 3.1 分块策略 + +文档切片是 RAG 系统的地基。切得不好，后面的检索和生成都受影响。 + +**固定大小分块**：按固定字符数或 token 数切分，实现简单但容易切断语义。比如"因意外伤害发生上述情形的，无等待期"这句话如果被切成两块，前半句在一块的末尾、后半句在另一块的开头，检索可能只命中其中一块，模型看到的就不完整。 + +**滑动窗口分块**：相邻块之间有重叠（通常 10%-20%），减少边界信息丢失。适合保险条款这种"前后文紧密关联"的文档。重叠太多会导致检索结果重复，占用 token 预算。 + +**语义分块**：按段落、章节等自然边界切分，保持每个块内的语义完整。Markdown 文档天然适合这种策略（按标题层级切分）。缺点是块大小不均匀，有的块可能过长。 + +**多粒度分块（Small-to-Big / Parent-Child）**：这是更进阶的策略。把文档切成两层： + +- **子块（Child Chunks）**：较小（200-500 token），用于向量检索。小块嵌入更聚焦，检索精度更高。 + +- **父块（Parent Chunks）**：较大（800-2000 token），包含子块的完整上下文。检索命中子块后，把对应的父块也一并取出来送给 LLM。 + +这种做法的好处是"检索用小块（精确）、回答用大块（完整）"。实验表明，子块检索 + 父块上下文的方案在准确率上比单一大块方案高 10%-15%。 + +**推荐起步方案**：按 Markdown 的 `##` 标题切分 + 滑动窗口，块大小控制在 800-1200 token，重叠 15%。有精力的话，升级到多粒度分块（子块 400 token 检索 + 父块 1200 token 回答）。 + +### 3.2 混合检索 + +单一检索策略各有盲区： + +- BM25 对精确匹配敏感（数字、条款编号），但无法理解"退保费用"和"退保手续费"是同一个东西 + +- 向量检索（Embedding）能捕捉语义相似性，但对数字和年份不够敏感 + +**混合检索** = BM25 + 向量检索，两路同时召回，结果融合后一起送进 LLM。融合方式： + +- **简单合并去重**：两路结果拼接，去重后按原始文档顺序排列 + +- **RRF（Reciprocal Rank Fusion）**：按每路结果的排名位置加权融合，不依赖绝对分数。公式：`score(d) = Σ 1/(k + rank_i(d))`，k 通常取 60 + +- **加权分数融合**：两路结果归一化后按权重加和（如 BM25 权重 0.3 + 向量 0.7），需要调参 + +实现要点： + +- BM25 用 `rank-bm25` 库，分词用 `jieba` + +- 向量检索可选 `text-embedding-3-small`（百炼提供）或本地模型 `bge-large-zh` + +- 两路各召回 20-30 个片段，融合后取 top 10-15 + +#### BGE 模型系列速览（扩展学习） + +注：经向赛题方确认，Embedding 模型和 Rerank 模型**不允许在正式提交中使用**。以下内容仅供扩展学习，帮助理解 RAG 系统的完整技术栈，不建议在正式方案中依赖。 + +BAAI（北京智源人工智能研究院）开源的 BGE 系列是目前中文 RAG 场景最常用的模型族，覆盖了 Embedding 和 Reranker 两类需求。了解各型号的差异，有助于根据实际算力条件选型： + +**Embedding 模型（向量检索）：** + +| 模型 | 参数量 | 特点 | 适用场景 | + +| --- | --- | --- | --- | + +| `bge-large-zh-v1.5` | 326M | 中文专用，C-MTEB 榜单前列 | 纯中文文档，CPU 可跑但推荐 GPU | + +| `bge-m3` | 568M | 多语言，支持 8192 token 长文本，可同时输出 dense + sparse 向量 | 中英混合文档，或需要长上下文嵌入 | + +| `bge-multilingual-gemma2` | 9B | 最新一代，MTEB 多语言榜首，精度最高 | GPU 充足时的最强选择 | + +**Reranker 模型（重排序）：** + +| 模型 | 参数量 | 特点 | 适用场景 | + +| --- | --- | --- | --- | + +| `bge-reranker-base` | 278M | 轻量，CPU 可跑，中文效果不错 | 算力受限时的默认选择 | + +| `bge-reranker-large` | 560M | 精度高于 base，需要 GPU | 有 GPU 时的升级选择 | + +| `bge-reranker-v2-m3` | 568M | 多语言，与 bge-m3 配套使用 | 中英混合文档的重排序 | + +对本赛题的建议：如果赛方给 GPU，`bge-m3`（Embedding）+ `bge-reranker-v2-m3`（Reranker）是多语言场景的稳妥组合；如果只有 CPU，`bge-large-zh-v1.5` + `bge-reranker-base` 可以在可接受的速度下完成检索和重排。 + +#### 视觉检索：跳过 OCR 的另一种思路 + +前面的所有方案都遵循同一个范式：PDF → 解析为文本 → 切片 → 文本检索。但如果 PDF 解析本身就是瓶颈（表格乱码、公式丢失、双栏错序），有没有可能直接跳过解析？ + +**ColPali**（2024）和 **ColQwen**（2025）[15] 提出了视觉检索的思路：把 PDF 页面渲染成图像，用视觉语言模型（VLM）直接从页面图像中提取 patch 级别的嵌入，然后基于这些视觉嵌入做检索。整个过程不需要 OCR、不需要版面分析、不需要 Markdown 转换。 + +具体做法： + +1. 把每页 PDF 渲染为高分辨率图像（如 448×448） + +1. VLM 对图像做 patch 编码（如 ColPali 产出每页 1024 个 patch embedding） + +1. 查询时，问题文本和页面图像的 patch embedding 做 MaxSim 匹配（取每个 query token 与所有 patch 的最大相似度之和） + +1. 返回最相关的页面 + +**优势：** + +- 完全绕过了 PDF 解析的所有问题：表格、公式、双栏都不需要处理 + +- 布局信息天然保留（表格的视觉结构、公式的排版位置） + +- ViDoRe 评测基准上，ColPali 的检索精度超越了传统 OCR + 文本检索方案 + +**局限：** + +- 需要 VLM（3B+ 参数），GPU 必须 + +- 返回粒度是整页，不是精确的文本片段：模型需要自己从页面图像中"读"出答案 + +- 目前以英文文档为主，中文文档的视觉检索效果待验证 + +- ColModernVBERT 提供了一个 250M 参数的轻量替代，精度接近 ColPali 但模型小 10 倍 + +**对本赛题的启示：** 视觉检索提供了一个激进的思路：如果 MinerU 或 Marker 的解析效果在特定文档上仍然不理想，可以直接把页面当图片检索，搭配多模态 LLM（如 GPT-4o、Qwen-VL）来"看图答题"。这在保险条款的复杂表格和公式场景下可能有奇效。但 GPU 要求和中文适配是两个现实障碍，建议作为实验性方向而非主方案。 + +### 3.3 重排序 + +初步检索的结果中可能混入弱相关片段。重排序用更精细的模型对候选片段逐一打分，把真正相关的排到前面。 + +常见方案： + +- Cross-Encoder 模型：把"问题 + 候选片段"拼接后送入模型，直接输出相关性分数。BGE Reranker 系列的具体型号选择见上节速览表。 + +- LLM-based Rerank：用 LLM 自己对候选片段打分。优点是灵活（可以按自定义标准评分），缺点是慢且消耗 token。 + +建议：如果使用百炼 Embedding API，可以搭配百炼的 Rerank API；如果本地部署，见上节 BGE 速览表按算力条件选择。 + +### 3.4 查询增强 + +查询增强策略（如 HyDE、查询改写、查询分解）旨在缩小用户口语化提问与文档专业术语之间的语义差距。具体方案见 3.6 节"前沿 RAG 范式"中的 HyDE 讨论。 + +### 3.5 片段组装 + +检索到的片段不能直接拼接就送给 LLM。需要注意： + +- **去重**：滑动窗口会导致相邻片段内容重叠，需要去重 + +- **排序**：按文档原始顺序排列片段（而不是按相关性分数），保持原文的逻辑流 + +- **截断**：总 prompt 长度受 token 预算限制，需要在片段数量和质量之间做取舍 + +### 3.6 前沿 RAG 范式 + +前面讲的分块、混合检索、重排序都属于"标准 RAG"的优化手段，在分块质量、检索精度、排序准确性上做改进。近一年来，RAG 领域出现了几个重要的新范式，它们不是调参数，而是改变了检索的底层结构：从"搜碎片"升级到"理解结构"。 + +理解这些范式的关键，在于搞清楚它们各自在解决什么问题。传统 RAG 有三个根本缺陷： + +1. **扁平化**：所有文档块在同一层，检索只看块级别的相似度，无法理解跨块的语义结构（一个公式分散在三段里，单块检索无法还原） + +1. **局部性**：擅长回答"这段话说了什么"，但回答不了"这些文档整体讲了什么"（四份保险条款的退保规则有什么系统性差异？） + +1. **无反思**：检索到什么就用什么，不评估质量，不修正方向 + +下面的范式各自针对其中一个或多个缺陷。 + +#### GraphRAG：用知识图谱做检索 + +**GraphRAG**（Microsoft Research, 2024 年 4 月发布, arXiv:2404.16130）[5] 的核心思路：不要只把文档切成碎片去搜，而是先从文档中提取实体和关系，构建知识图谱，再基于图谱做检索和推理。 + +传统 RAG 本质上在做"向量空间中的邻居搜索"：把问题和文档块都映射到向量空间，找距离最近的块。这种做法的上限是"找相似的文本片段"，但无法回答需要跨片段推理的问题。比如"这四份保险条款中，哪份对意外身故的赔付最高？"，这个问题需要先分别理解四份条款的身故保险金计算规则，再横向比较，而不是找到某个"相似"的片段就够了。 + +**GraphRAG 的完整流程：** + +1. **实体与关系抽取**：用 LLM 从源文档中提取实体和关系。实体可以是产品名（"平安智盈金生"）、概念（"现金价值"）、数值（"75%"）、条款编号（"第 7 保单年度"）；关系描述实体之间的逻辑连接（"现金价值 → 计算公式 → 累计保费 × 75%"、"意外身故 → 适用规则 → 无等待期"）。 + +1. **知识图谱构建**：将抽取出的实体作为节点、关系作为边，构建一张有向/无向图。这张图不是简单的关键词共现网络，而是携带了语义信息的知识结构。 + +1. **社区检测**：用 Leiden 算法在图结构上发现紧密关联的实体群组（社区）。每个社区对应一个主题领域，比如"退保规则社区"可能包含"退保费用""退保费用率""现金价值""保单年度"等实体及其关系。 + +1. **社区摘要生成**：对每个检测出的社区，用 LLM 生成一个总结性的摘要，描述这个社区的核心信息。这一步是 GraphRAG 区别于传统图方法的关键：不是把原始文本片段直接存到图里，而是对每个主题生成高层摘要，使得检索时可以直接读取"关于退保规则的核心要点是什么"。 + +1. **两种检索模式**： + +1. **Local Search（局部搜索）**：查询时匹配相关实体，从实体出发沿图的边扩散到邻居节点，获取与问题直接相关的实体和关系。适合事实查找类问题（"平安智盈金生的现金价值怎么算？"）。 + +1. **Global Search（全局搜索）**：查询时匹配相关社区，用社区的摘要生成部分回答，再将所有部分回答汇总成最终答案。适合全局理解类问题（"四份条款的退保规则有什么系统性差异？"）。这一步本质上是 Map-Reduce：社区摘要做 Map，LLM 汇总做 Reduce。 + +**对本赛题的适用性：** + +跨文档对比题和多文档联合推理题是 GraphRAG 的最优场景。比如 ins_a_001（四产品身故保险金排序），传统 RAG 需要逐份文档检索每份产品的身故保险金规则，再在 prompt 里手动对比。GraphRAG 可以在知识图谱中直接查询"身故保险金"这个实体，沿关系边找到四个产品的计算规则，甚至社区摘要已经预先生成了"四产品身故保险金对比"的总结。 + +代价是预处理阶段需要大量 LLM 调用：实体抽取（每份文档）、社区摘要（每个社区）。对于本赛题的 100 题对应的文档集，这个开销是可控的，大约几十到几百次 LLM 调用，可以在提交前离线完成，不占用正式提交的 Token 预算。 + +**实现参考：** 微软开源了 [graphrag](https://github.com/microsoft/graphrag) Python 包，支持本地运行。对于本赛题的中文金融文档，需要替换内置的实体抽取 prompt 为中文版本。 + +#### LightRAG：轻量级图 RAG + +**LightRAG**（香港大学 HKUDS 实验室, 2024 年 10 月发布, EMNLP 2025, arXiv:2410.05779）[6] 是 GraphRAG 的轻量级替代方案，保留了图结构的优势，但大幅降低了构建成本和检索延迟。 + +**与 Microsoft GraphRAG 的关键差异：** + +GraphRAG 的社区检测 + 逐社区摘要是一个重操作：Leiden 算法需要遍历整张图，每个社区的摘要又需要独立的 LLM 调用。LightRAG 不依赖社区检测，而是用**双向量索引**来同时支持局部和全局检索： + +1. **实体向量索引**：每个实体节点有独立的向量表示（由实体名称和描述的 embedding 组成）。查询时，问题向量与实体向量做相似度匹配，找到相关实体后沿关系边扩散到邻居节点。这对应 GraphRAG 的 Local Search。 + +1. **关系向量索引**：每条关系边也有独立的向量表示（由关系描述和两端实体信息的 embedding 组成）。查询时，问题向量与关系向量做相似度匹配，直接找到相关的关系。这能捕捉到跨实体的语义模式（比如"退保费用率逐年递减"这种趋势性关系），而不仅仅是单个实体的匹配。 + +1. **关键词引导查询**：LightRAG 从用户问题中提取关键词，将这些关键词的向量拼接成组合查询向量。这种方式比直接用整个问题做向量检索更聚焦，减少了语义稀释。 + +**增量更新能力：** + +LightRAG 的图结构支持增量插入——新文档加入时，只需要提取新文档的实体和关系、计算它们的向量、插入图结构中，不需要重建已有索引。这对本赛题可能很重要：A 榜只有 100 题对应的一批文档，如果 B 榜下发新文档，LightRAG 可以直接增量更新，而 GraphRAG 需要重新跑社区检测。 + +**对本赛题的定位：** + +LightRAG 适合作为从标准 RAG 向图 RAG 过渡的第一个实验方案。它的构建成本接近传统 RAG（主要开销在实体抽取，不需要社区检测），但能同时支持局部事实查找和全局对比推理。如果本赛题后续出现需要跨文档联合推理的难题，LightRAG 的性价比优于全量 GraphRAG。 + +**实现参考：** 开源项目 [HKUDS/LightRAG](https://github.com/HKUDS/LightRAG) 提供了完整的 Python 实现。 + +#### StructRAG：按任务类型选择知识结构 + +**StructRAG**（2024 年发布）[7] 提出了一个有意思的视角：不同任务需要不同的知识组织方式，不应该统一处理。 + +它定义了五种结构类型，根据任务特征自动选择最合适的结构： + +| 结构类型 | 适用任务 | 组织方式 | + +| --- | --- | --- | + +| 表格（Table） | 统计类任务 | 结构化行列 | + +| 图（Graph） | 长链推理任务 | 实体-关系网络 | + +| 算法（Algorithm） | 规划类任务 | 步骤流程 | + +| 目录（Catalog） | 总结类任务 | 层级分类 | + +| 片段（Chunk） | 简单单跳问答 | 原始文本块 | + +对本赛题的启示：不同题型应该用不同的知识组织方式。事实查找题用片段检索就够了；计算题需要从文档中提取公式构建成"算法"结构；跨文档对比题需要图结构。一个统一的检索框架可能不如按题型分派检索策略来得有效。 + +#### RAPTOR：树状递归摘要检索 + +**RAPTOR（Recursive Abstractive Processing for Tree-Organized Retrieval）**（2024 年 1 月发布, arXiv:2401.18059）[8] 解决的是"扁平检索丢失全局上下文"的问题，思路和 GraphRAG 不同：GraphRAG 用图结构，RAPTOR 用树结构。 + +**为什么需要树结构：** + +传统 RAG 把所有文档块平铺在一层。检索时只看单个块的相似度，但有些问题的答案跨越了多个块。一个典型的保险条款场景：某产品的退保规则可能分布在"第二章 现金价值"（定义公式）、"第五章 退保"（退保流程）、"附录"（费率表）三个不同章节。这三个章节在文档中相距很远，但在语义上紧密关联。扁平检索很难同时召回这三个章节的片段。 + +**RAPTOR 的构建流程：** + +1. **叶子层**：将文档切成基础块（如每页 Markdown），每个块做 embedding + +1. **聚类**：用高斯混合模型（GMM）对叶子节点的 embedding 做软聚类，每个节点可以属于多个聚类 + +1. **摘要**：对每个聚类，用 LLM 对聚类内的所有文本块生成一个摘要。这个摘要成为上一层的一个节点 + +1. **递归**：对上层节点重复聚类 + 摘要，直到只剩一个根节点 + +1. **最终结构**：一棵多层树，底层是原始文本块（精确细节），中层是主题摘要（如"退保规则""身故保险金规则"），顶层是全局摘要（如"该产品的核心条款概述"） + +**检索时的层级选择：** + +- 简单事实查找（"平安智盈金生第 7 年的现金价值是多少？"）→ 从叶子层检索，直接拿精确数值 + +- 中等复杂度（"该产品的退保规则有哪些注意事项？"）→ 从中层摘要节点检索，获取主题级别的完整信息 + +- 全局理解（"该产品的整体保障结构是怎样的？"）→ 从顶层摘要节点检索 + +**变体与改进：** + +- **adRAP**（Adaptive RAPTOR）：解决 RAPTOR 在动态数据集上的问题。原始 RAPTOR 每次新增文档都需要重建整棵树（因为聚类会变）。adRAP 通过增量调整聚类结构来避免全量重建，适合文档持续增加的场景。 + +- **postQFRAP**：不改变检索端的索引结构，而是在检索结果回来之后，对召回的片段做一次"查询聚焦的递归摘要处理"：把检索结果按 RAPTOR 的思路聚类+摘要，生成一个结构化的上下文再送给 LLM。优势是可以作为后处理模块接入任何检索管线。 + +**对本赛题的适用场景：** + +行业研报类题目（如 res_a_001 银保渠道发展历程）和跨年度对比题（如 fin_a_001 比亚迪两年年报对比）是 RAPTOR 的优势场景：这些题需要同时理解宏观趋势和微观数据，树结构天然匹配"总-分"的信息粒度。 + +#### Self-RAG 与 CRAG：让模型反思检索质量 + +传统 RAG 无条件信任检索结果：搜到什么就用什么，不管质量如何。这在实践中是个严重问题：BM25 可能因为关键词匹配把不相关的片段排到前面，向量检索可能因为语义模糊召回弱相关的内容。Self-RAG 和 CRAG 在这里加了"自我评估"机制。 + +**Self-RAG**（2024 年 10 月发布, arXiv:2310.11511）[9] 的核心是"检索 + 反思 + 重试"循环： + +1. 按需检索：不是每个问题都需要检索。Self-RAG 先判断当前问题是否需要检索（简单常识问题可能不需要） + +1. 检索文档片段 + +1. 对每个片段做三方面评估：相关性（这个片段和问题相关吗？）、支持度（这个片段支持还是反对某个观点？）、有用性（这个片段对回答有帮助吗？） + +1. 对低质量片段重新检索或修正 + +1. 生成最终答案时，标注每个论断的来源片段和可信度 + +**CRAG（Corrective RAG）**（2024 年 1 月发布, arXiv:2401.15884）[10] 在 Self-RAG 基础上更进一步，加入了自动纠错机制： + +1. 检索文档片段 + +1. 用检索质量评估器对整体检索结果打分（0-1 分） + +1. 根据分数分三级处理： + +1. 分数 ≥ 0.7（CORRECT）：检索质量好，直接使用 + +1. 分数 0.3-0.7（AMBIGUOUS）：质量一般，保留当前结果同时用不同关键词补充检索，两轮结果合并 + +1. 分数 ≤ 0.3（INCORRECT）：质量差，丢弃检索结果，切换到全文阅读或其他策略 + +1. 最终生成前再做一次"幻觉检查"，过滤掉检索结果中不存在的论断 + +**CRAG 的关键洞察：** 检索质量的评估不需要复杂的模型。用检索结果中每个片段与问题的关键词重叠度、片段间的语义一致性等轻量特征，就能大致判断检索质量。只有当评分落在中间区域时才需要 LLM 介入做进一步判断。 + +**对本赛题的适配：** + +本赛题不允许网络搜索，但 CRAG 的三级处理思路可以直接适配： + +- CORRECT → 直接用检索结果生成答案 + +- AMBIGUOUS → 换关键词重新检索同一文档集，或者扩大检索范围（如从只搜前 10 个块扩展到搜全部块） + +- INCORRECT → 放弃检索，改为读全文（回到 baseline 的硬截断方案作为 fallback） + +这个机制的价值在于：它让系统在"检索结果好"的时候享受检索的效率，在"检索结果差"的时候自动回退到更稳健的方案，而不是一味信任检索。 + +#### HyDE：用假设答案做检索 + +**HyDE（Hypothetical Document Embeddings）**（2023 年 12 月发布, arXiv:2212.10496）[11] 解决的是"用户问题用词和文档用词不一致"导致检索失败的问题。 + +**问题场景：** 用户问"怎么退保最划算"，文档里写的是"现金价值计算方式"和"退保费用率表"。问题中的"划算"是口语化表达，文档中的"现金价值"是专业术语，两者在向量空间中距离很远，直接向量检索可能召回到完全不相关的内容。 + +**HyDE 的做法：** 先让 LLM 凭空生成一份假想的"理想答案"，再把这份假想答案（而非原始问题）转成向量去检索。假想答案是用文档风格写的（包含"现金价值""退保费用""已交保费"等专业术语），所以更容易匹配到真实的相关片段。 + +流程：用户问题 → LLM 生成假设答案 → 假设答案 Embedding → 向量检索 → 返回真实片段。 + +代价是每次检索多一次 LLM 调用（生成假设答案），但检索命中率提升显著：在多个 benchmark 上，HyDE 的召回率比直接向量检索高 10%-20%。对本赛题的口语化问题（如"哪个产品最划算"）特别有效。 + +**HyDE + 混合检索的实践建议：** 根据"Searching for Best Practices in RAG"（2024 年 7 月发布, arXiv:2407.01219）[12] 的系统评测，**HyDE + 混合检索（BM25 + 向量）** 是目前综合表现最好的默认配置。该评测在多个 benchmark 上对比了不同检索策略，HyDE + Hybrid Search 的检索质量显著优于单独的查询改写（Query Rewriting）或查询分解（Query Decomposition）。 + +#### KAG：专业领域知识增强 + +**KAG（Knowledge Augmented Generation）**（2024 年发布）[14] 针对的是专业领域 RAG 的一个特殊问题：通用知识图谱的实体和关系定义与专业领域不匹配。 + +例如，用通用 OpenIE 工具从保险条款中抽取实体，可能把"现金价值"标注为普通名词而非专业概念，把"第 7 保单年度"拆成两个无关实体。KAG 的做法是引入**领域知识对齐**：用领域概念体系（如保险术语表）来指导实体抽取和关系定义，减少信息抽取过程中的噪音。 + +对本赛题的启示：如果使用 GraphRAG/LightRAG 做实体抽取，需要定制实体类型（产品名称、条款编号、计算公式、数值、条件判断）和关系类型（属于、计算依据、例外条件、时间约束），而不是用通用 prompt。否则抽取出的实体关系图可能充满噪音，反而干扰检索。 + +#### 多跳推理 RAG（Multi-hop RAG） + +多跳推理是指答案不能从单个文档片段直接获得，需要通过多个片段的推理链来推导。本赛题中这类问题很常见："某产品在第 N 年的退保金额是多少？"需要先找到退保公式（第一跳），再找到该年度的退保费用率（第二跳），再找到该年度的现金价值（第三跳），最后代入计算。 + +传统 RAG 对多跳问题的处理很弱：检索可能只召回第一跳的片段（退保公式），但漏掉了后两跳（费用率和现金价值）。针对多跳 RAG 的改进方案： + +- **Iterative RAG（迭代检索）**：第一轮检索 → 生成部分答案 → 根据部分答案生成新查询 → 第二轮检索 → 汇总。每轮检索聚焦一个子问题 + +- **IRCoT（Interleaving Retrieval with Chain-of-Thought）**：把思维链推理和检索交替进行。每推一步就检索一次，用检索结果来验证和修正推理方向 + +- **Beam Search RAG**：对多跳路径做束搜索，同时探索多条推理链，最后选证据最充分的一条 + +#### Agentic RAG：让 Agent 自主编排检索策略 + +前面所有的 RAG 范式都预设了检索策略：什么时候搜、搜什么、搜几次，都是提前写死的。Agentic RAG 的思路是把这个决策权交给 LLM 自己：通过 Function Calling，LLM 自主决定： + +- 当前问题需要检索吗？（简单常识题可能不需要） + +- 搜什么关键词？（可能需要多轮尝试不同关键词） + +- 搜到了什么程度才算足够？（自主判断信息是否足够） + +- 搜到的东西质量差怎么办？（自动切换策略） + +这和本赛题的 Agent 定位高度一致。Baseline 测试过 Function Calling，Token 消耗比 no-tool 高约 18%，但 FC 的灵活性（模型可以自主决定"什么时候搜、搜什么"）是提升准确率的关键路径。具体方案见第六章"Agent 化方向"。 + +### 3.7 本赛题的 RAG 路线建议 + +从 baseline 的硬截断出发，建议按以下顺序逐步引入 RAG 优化： + +| 阶段 | 方案 | 预期收益 | 风险 | + +| --- | --- | --- | --- | + +| 1 | BM25 检索 + 语义分块 | 解决"答案不在前 4000 字"的问题 | 连贯性断裂，可能降分 | + +| 2 | + 多粒度分块（子块检索 + 父块回答） | 缓解连贯性断裂 | 实现复杂度增加 | + +| 3 | + 混合检索（BM25 + 向量）+ HyDE | 提升召回率，改善口语化问题匹配 | 需要 Embedding 模型，HyDE 增加一次 LLM 调用 | + +| 4 | + Rerank 精排 | 提升精确率 | 额外计算开销 | + +| 5 | + 动态记忆压缩（Snip + Memento + 结构化摘要） | 在有限 Token 预算内保留最多有效信息，多文档场景收益明显 | 压缩策略调优需要实验，过度压缩可能丢失关键条件 | + +| 6 | + Bash 工具（Python 代码执行做数值计算） | 消灭公式计算题的算术错误 | 需要设计沙箱执行环境 | + +| 7 | + CRAG 自检机制（质量评估 + 自动回退） | 减少低质量检索导致的错误 | 需要设计评估器逻辑 | + +| 8 | LightRAG（跨文档题） | 解决跨文档联合推理，支持增量更新 | 预处理 Token 消耗 | + +| 9 | Agentic RAG（FC 自主编排检索策略） | 灵活应对不同题型 | Token 消耗增加约 18%，需要调优 FC 行为 | + +| 10 | smolagents Code Agent（Python 代码编排工具调用） | 减少 30% 推理步骤，精准数值计算 | 需要学习 smolagents 框架，代码生成质量依赖模型能力 | + +建议每完成一个阶段就跑一次 A 榜提交，验证实际收益，避免"加了优化反而降分"的陷阱。 + +## 4 上下文管理与动态记忆压缩 + +赛题名称里就有"动态记忆压缩"，这其实定义了本赛题的核心技术挑战。前面三章（PDF 解析、RAG 检索）解决的是"怎么找到有用信息"，这一章解决的是"怎么在有限 Token 预算内把最重要的信息喂给模型"。 + +### 4.1 问题的本质：上下文窗口是稀缺资源 + +每道题的推理链路可以拆成：读文档 → 提取信息 → 判断 → 输出答案。每一步都消耗 Token，但预算有限（赛方总预算 500 万 Token）。这意味着不能把所有文档全读一遍，也不能把检索结果全塞进 prompt。 + +上下文管理的核心问题可以表述为：**给定 Token 预算 B，如何从候选信息池中选择一个子集，使得模型回答正确的概率最大化？** + +候选信息池包括： + +- 文档原文片段（检索结果） + +- 上一步推理的中间结果 + +- 题目本身和选项 + +- Prompt 模板 + +- 验证/自检的二次推理结果 + +### 4.2 动态截断 + +Baseline 的硬截断固定取前 4000 字符。更好的策略是让模型自己决定什么时候停止阅读。 + +实现思路：分段读取文档，每读一段就让模型判断"当前信息是否足够回答问题"。如果足够就停止，不够就继续读下一段。这本质上是 Agent 化的第一步。 + +更精细的做法是**按题型设定不同的阅读预算**。事实查找题可能读前 2000 字符就能找到答案；跨文档对比题需要读多份文档，每份可能 1000 字符就够；公式计算题只需要定位到公式所在的段落，读到公式后就可以停。 + +### 4.3 信息密度控制 + +保险条款和监管法规中有大量模板化文本（如"本合同的构成""投保须知"等），这些内容对答题几乎没有价值，但占用了大量 token。 + +可以预处理时过滤掉高频无意义段落，或者在检索时对"释义""名词定义"等章节降权。更系统化的做法是**按题型定义信息价值函数**：对计算题，公式段落的权重最高；对条款定位题，例外条件段落的权重最高。 + +### 4.4 动态记忆压缩：分层存储与选择性遗忘 + +这才是本赛题"动态记忆压缩"的核心。前面的截断和密度控制都是静态策略，动态压缩需要在推理过程中持续做决策：什么该记住、什么该压缩、什么该丢弃。 + +#### 工业界的实践：Agent 的记忆分层 + +几个主流 Agent 框架都有类似的记忆管理机制，核心思路都是**分层存储**： + +**Kimi Code CLI 的 Context Compaction** [19] + +当对话历史超过 token 阈值时自动触发 compaction，让 LLM 将历史对话压缩为结构化摘要，按优先级保留： + +1. **Current Task State**（当前任务状态：正在做什么） + +1. **Errors & Solutions**（遇到的错误和解决方案） + +1. **Code Evolution**（代码最终版本，丢弃中间尝试） + +1. **System Context**（项目结构、环境配置） + +1. **Design Decisions**（架构决策及理由） + +1. **TODO Items**（未完成的任务） + +Compaction 前后有 PreCompact / PostCompact hooks 可插拔，允许用户自定义压缩策略（如阻止特定信息的压缩）。压缩后的历史替换原始对话，模型只看到摘要。 + +这个机制对本赛题的启发：压缩的核心是有选择地保留、改写、丢弃。Kimi Code 的 6 级优先级给出了一个信息价值排序的框架。对于本赛题，我们可以定义类似的优先级： + +1. 题目的完整文本和所有选项（最高优先级，绝不压缩） + +1. 从文档中提取出的公式、数值、条件判断（核心证据） + +1. 检索到的原文片段中与题目直接相关的部分 + +1. 文档章节标题和结构信息（用于定位和引用） + +1. 中间推理过程的摘要 + +1. 检索到的原文中经判断为不相关的部分（最低优先级，优先丢弃） + +**Hermes 的 Trajectory Compressor** [20] + +Hermes 是面向通用 Agent 任务的框架，其 `trajectory_compressor.py` 实现了一套完整的对话历史压缩管线，虽然是为训练数据压缩设计的，但核心算法可以直接迁移到推理场景。 + +压缩算法的完整流程： + +**第一步：计算压缩需求。** 遍历 trajectory 中每个 turn，用 tokenizer 计数。如果总 tokens 未超过目标上限（默认 15250），直接跳过压缩。 + +**第二步：确定受保护区域。** 在 trajectory 中标记不可压缩的 turns： + +- 头部保护：第一个 system 消息、第一个 human 消息、第一个 gpt 回复、第一个 tool 调用。这些是任务的初始上下文，丢弃后模型会丢失任务定义。 + +- 尾部保护：最后 N 个 turns（默认 N=4）。这些包含最新的推理状态和即将做出的决策。 + +**第三步：计算压缩区域。** 受保护头部之后、受保护尾部之前的所有 turns 构成"可压缩区域"。从这个区域的起始位置开始，逐 turn 累积 tokens，直到累积量满足： + +``` +累计 tokens ≥ (总 tokens - 目标上限) + 摘要目标 tokens +``` + +如果遍历完整个可压缩区域仍不满足，则压缩整个区域。 + +**第四步：LLM 生成摘要。** 将被压缩的 turns 拼接为一段文本，发送给单独的摘要模型（默认 gemini-3-flash，temperature=0.3）。摘要 prompt 明确要求包含： + +- 助手执行了哪些操作（工具调用、搜索、文件操作） + +- 获得了什么关键信息或结果 + +- 做出了什么重要决策或发现 + +- 相关的数据、文件名、数值、输出 + +摘要以 `[CONTEXT SUMMARY]:` 为前缀，目标长度 750 tokens。 + +**第五步：组装压缩后的 trajectory。** 头部 turns（原文保留）+ 摘要消息（作为 human 消息插入）+ 尾部 turns（原文保留）。如果 system 消息中配置了 `add_summary_notice`，会在 system prompt 末尾追加提示文字，告知模型部分历史已被压缩。 + +**压缩效果度量：** 每条 trajectory 压缩后记录详细 metrics： + +- `compression_ratio`：压缩后 tokens / 原始 tokens + +- `tokens_saved`：节省的 tokens 数 + +- `turns_removed`：被移除的 turns 数 + +- `still_over_limit`：压缩后是否仍超预算 + +**对本赛题的关键启发：** + +Hermes 压缩器给了一个精确的算法框架：**在 token 预算约束下，保护首尾、压缩中间、LLM 替代**。迁移到本赛题的单题推理场景： + +- **"首"是什么？** 题目文本 + 选项（不可压缩，必须完整保留） + +- **"尾"是什么？** 最终推理步骤（列出公式 → 代入数值 → 计算结果 → 对比选项 → 输出答案）。这一段的推理链必须完整保留，因为任何压缩都可能导致逻辑跳跃 + +- **"中间"是什么？** 文档阅读过程。比如读了一份 50 页的保险条款，中间可能读了 20 页才找到"现金价值"的定义。这 20 页的阅读过程可以压缩为一段结构化摘要："该产品的现金价值计算规则：第 1-5 年为已交保费 × 100%，第 6-10 年为已交保费 × 75%，第 10 年后为已交保费 × 50%。退保费用率为逐年递减，第 7 年起为 0%。" + +- **摘要替代**：用 LLM（或同一个小模型）生成文档阅读摘要，替代原始阅读内容。摘要的关键要求是：**结构化、数值精确、标注出处（页码/段落号）**。 + +**Claude Code 的四层渐进压缩** [21] + +Claude Code 把压缩拆成四层，从便宜到贵逐级尝试： + +``` +Layer 1: Snip（裁剪工具输出）→ 免费，不调 LLM +Layer 2: Microcompact（基于 prompt cache 的轻量压缩） +Layer 3: Context Collapse（折叠搜索/读取操作为摘要） +Layer 4: Autocompact（LLM 全量总结）→ 最贵 +``` + +核心设计原则：只有低层搞不定才升级。Snip 只是把老工具结果截断为头尾各保留一部分，不消耗 token；Autocompact 要调一次 LLM 生成全量摘要。如果 80% 的场景 Snip 就够了，只有 20% 需要 LLM 介入，整体 token 效率远高于每次都调 LLM。 + +对本赛题的启发：检索返回的文档片段如果太长，先 Snip（只保留包含关键词的段落 + 前后各一段上下文）；Snip 后仍然超预算再调 LLM 做摘要。不要一上来就让模型总结。 + +**Codex 的 Memento 策略：为什么不能只留摘要** [22] + +Codex 的 compaction 有一个关键设计：压缩后的历史不是纯摘要，而是 `initial_context + 精选 user 消息 + summary + 最近 N 条`。原因很简单，LLM 生成的 summary 会丢失精确信息。 + +一个本赛题中的典型场景：保险条款里"因意外伤害发生上述情形的，无等待期"这句例外条件，压缩时可能被总结为"等待期内一般不赔"，"意外伤害例外"这个关键信息就丢了。如果后续推理依赖这个例外条件判断选项对错，摘要导致的错误不可逆。 + +Codex 的做法是：user 原始消息尽量保留（最多 20k tokens），只在实在放不下时才把最早的 user 消息纳入摘要。摘要负责"大局观"（任务进展、关键决策），原始消息负责"精确性"（具体数值、条件判断、例外条款）。 + +对本赛题来说，这意味着压缩时至少要保留三类信息不被摘要化： + +1. **题目原文**（包括选项）：压缩题目等于修改题目 + +1. **从文档中提取的精确数值和公式**：如"现金价值 = 累计保费 × 75%" + +1. **包含例外条件的条款原文**：如"但意外伤害导致的情形除外" + +**generic-agent 的 L1-L4 记忆层级** [24] + +这是最系统的分层方案，来自一个面向长期自主运行的 Agent 项目。虽然不是专门为本赛题设计的，但其分层哲学和信息分类决策树可以直接借用。 + +**四层架构的完整定义：** + +**L1：全局内存索引（**`**global_mem_insight.txt**`**）** + +硬约束 ≤30 行、期望 <1k tokens。内容只包含： + +- **场景关键词 → 记忆定位映射**：高频场景直接给出 SOP 文件名或 L2 节名；低频场景仅列关键词，需要时再 read L2 或 ls L3 + +- **RULES（避坑准则）**：压缩版的红线规则和高频犯错点。每条不超过一句话，如"搜索用 google 不用百度""禁无条件杀 python（会杀自己）" + +L1 的更新纪律极严：L2/L3 有新增/删除时才更新 L1；修改时不允许 overwrite 或 code run，只能少量 patch。不写 How-to、不写技术细节、不写日志。 + +**L2：全局事实库（**`**global_mem.txt**`**）** + +存储环境特异性事实：IP、非标路径、凭证、配置常量等。按 `## [SECTION]` 组织。这些是 LLM 零样本无法推理出来的信息。禁止存储易变状态、猜测、或通用常识。 + +**L3：任务级精简记录库（**`**../memory/**`**）** + +为特定任务保留的极简 SOP 和工具脚本。只记录跨会话仍重要、且难以通过少量探测快速重建的要点： + +- 该任务特有的隐藏前置条件 + +- 典型易踩坑点（一旦遗忘会导致高成本重试的信息） + +- 高复用的处理脚本（封装复杂逻辑，避免每次重新推理） + +不记录普通操作步骤或可在几步探测中重新获得的路径信息。 + +**L4：历史会话层（**`**L4_raw_sessions/**`**）** + +由调度器自动收集的原始对话记录，用于回溯过往上下文。这一层不主动维护，只在需要时被检索。 + +**信息分类决策树：** + +``` +"这条信息该放哪层？" + +是环境特异性事实（IP、凭证、ID 等）？ +├─ YES → L2，然后按频率归入 L1 +└─ NO → 是通用操作规律（全局避坑准则）？ +├─ YES → L1 RULES（仅限 1 句压缩准则） +└─ NO → 是特定任务技术（艰难尝试才成功，未来还能用）？ +├─ YES → L3（专项 SOP 或脚本） +└─ NO → 丢弃（判定为通用常识或冗余信息） +``` + +**核心公理：** + +1. **行动验证原则**：任何写入 L1/L2/L3 的信息，必须源自成功的工具调用结果。严禁将模型的固有知识、推理猜测、未执行的计划作为事实写入。口号：No Execution, No Memory。 + +1. **神圣不可删改性**：凡是经过行动验证的有效配置、避坑指南、关键路径，在重构时严禁丢弃。可以压缩文字、可以迁移层级（从 L2 移到 L3），但不能丢失信息的准确性和可追溯性。 + +1. **禁止存储易变状态**：当前时间戳、临时 Session ID、运行中的 PID、某个具体绝对路径。这些随时间/会话高频变化的数据严禁写入记忆。 + +1. **最小充分指针**：上层只留能定位下层的最短标识，多一词即冗余。 + +**对本赛题的启发：** + +L1-L4 的分层哲学可以直接映射到单题推理的信息管理： + +- **L1 等价物**：题目的结构化索引，"这道题属于公式计算题，涉及文档 A 的现金价值公式和文档 B 的退保费用表"。这是一句话的摘要，帮助模型快速定位需要精读的文档段落。 + +- **L2 等价物**：从文档中提取的"硬事实"：公式、数值表、条款编号、条件判断。这些是 LLM 无法凭空生成的，必须从文档中精确提取。 + +- **L3 等价物**：针对特定题型的推理 SOP，比如"计算题 SOP：1) 定位公式 → 2) 提取参数 → 3) 代入数值 → 4) 计算结果 → 5) 匹配选项"。每类题型对应一套 SOP。 + +- **L4 等价物**：完整的文档原文（不压缩，保留在外部存储中，只在需要精确引用时调取）。 + +"行动验证原则"在本赛题中的对应是：**只有从文档原文中精确匹配到的信息才能作为答案依据**。模型的推理猜测（"看起来像是 75%"）不能作为证据，必须有原文引用。 + +"最小充分指针"的对应：在对比多份文档时，带着每份文档的结构化摘要（"产品 X 的身故保险金 = max(已交保费, 现金价值)"），摘要中标注原文出处（页码/段落号），需要验证时再回溯原文，不需要带着完整原文。 + +#### 学术界的方案：MemGPT 与记忆管理研究 + +**MemGPT**（UC Berkeley, 2023）[16] 提出了一个根本性的视角转换：给 LLM 一个分层的记忆系统，让它自己管理"什么在窗口内、什么在窗口外"，而不是试图扩展上下文窗口本身。 + +MemGPT 的核心类比是操作系统的虚拟内存管理： + +- **主上下文（Main Context）**：LLM 的固定上下文窗口，类比物理 RAM。容量有限（如 4K/8K/32K tokens），但访问速度最快。LLM 在这里做推理。 + +- **外部上下文（External Context）**：几乎无限的长期存储，类比磁盘。存储完整的对话历史、文档、用户信息等。访问速度慢（需要检索），但容量几乎不受限制。 + +**MemGPT 的关键创新在于：LLM 自己控制"换页"过程。** 传统做法是开发者写死截断策略（取最后 N 轮对话），MemGPT 的做法是让 LLM 通过 Function Calling 主动管理记忆： + +- 当主上下文快满时，LLM 可以调用 `conversation_search` 或 `archival_memory_search` 来检索需要的信息 + +- LLM 自己判断当前上下文中哪些信息不再需要，将其"换出"到外部存储 + +- LLM 自己判断下一步推理需要哪些外部信息，将其"换入"主上下文 + +这本质上是把记忆管理从"系统层的固定规则"升级为"应用层的智能决策"。 + +**MemGPT 的记忆类型：** + +1. **会话记忆（Conversational Memory）**：对话历史的滚动窗口。超出窗口的历史被自动分页存储，LLM 通过 `conversation_search` 函数按日期或关键词检索。 + +1. **档案记忆（Archival Memory）**：文档、文章、知识库等外部资料。LLM 通过 `archival_memory_search` 函数做语义检索。这部分最接近本赛题的"文档检索"场景。 + +1. **工作记忆（Working Memory）**：当前推理状态。LLM 可以通过 `core_memory_append` 和 `core_memory_replace` 函数主动更新（比如"用户刚才说了喜欢 Python，我把这个记下来"）。 + +**后续发展：** + +- **MemoryOS** [25] 在 MemGPT 基础上扩展为完整的记忆操作系统，引入记忆的热度机制（频繁访问的信息自动提升到更快的存储层）、用户画像的持续更新、以及记忆的主动遗忘策略（基于艾宾浩斯遗忘曲线）。 + +- **A-Mem（Agentic Memory）** [26] 进一步引入了结构化笔记和知识网络：LLM 动态生成结构化的笔记，并在笔记之间建立链接，形成一个可演化的知识网络，而不是简单地把信息存为文本块。 + +**对本赛题的迁移：** + +MemGPT 的主上下文 / 外部上下文双层模型可以直接映射到本赛题的推理架构： + +| MemGPT 概念 | 本赛题等价物 | + +| --- | --- | + +| 主上下文 | 当前题目的 prompt（题目 + 检索结果 + 推理过程），受单题 Token 预算约束 | + +| 外部上下文 | 文档全文（已解析为 Markdown 的完整内容），不受单题 Token 预算约束 | + +| 换页决策 | 模型判断当前检索结果是否足够回答问题，不够则从文档中检索更多内容 | + +| 会话记忆 | 前一道题的推理结果（如果有跨题关联） | + +| 档案记忆 | 文档的 BM25/向量索引 | + +关键差异：本赛题是"长文档 + 短推理"，MemGPT 是"短文档 + 长对话"。所以 MemGPT 的"换页"逻辑在本赛题中应该调整为"分段读取 + 渐进压缩"：把读过的文档段落压缩后换出，释放上下文空间给下一个段落。 + +具体来说，读一份 50 页的保险条款时： + +1. 读第 1-5 页 → 提取关键信息（产品名称、保险期间、基本保额）→ 压缩为结构化摘要，存入"外部上下文"的等价物（Python 变量或临时文件） + +1. 读第 6-10 页 → 提取退保规则 → 追加到摘要 + +1. 读第 11-15 页 → 发现是模板化条款（释义、投保须知）→ 跳过，不消耗主上下文 + +1. ...继续，直到模型判断信息足够回答问题 + +1. 最终提交给 LLM 的是结构化摘要而非 50 页原文 + +#### 对本赛题的迁移 + +上述所有方案都针对"长对话"场景，但本赛题的场景是"长文档 + 短推理"：每道题是一轮独立的推理，不需要跨题记忆。动态压缩在本赛题中应该用在**单题推理链的内部**： + +1. **文档阅读阶段**：分段读取，每读一段就压缩提取关键信息（公式、数值、条件判断），只保留压缩后的摘要进入下一段 + +1. **多文档对比阶段**：读完文档 A 后生成结构化摘要（如"产品 X：身故保险金 = max(已交保费, 现金价值)"），读文档 B 时只带着 A 的摘要而非原文，对比时用摘要即可 + +1. **验证阶段**：自检时不需要重新读全文，只需要带上第一轮推理的结构化摘要（公式 + 代入值 + 计算结果）做交叉验证 + +具体实现可以参考 Hermes 的 trajectory compressor 思路：保护题目的完整文本（head），对中间文档阅读过程做压缩（middle），保留最终推理步骤的完整链（tail）。 + +### 4.5 实现建议：从策略到代码 + +上面讨论的方案要落地到本赛题，需要回答三个具体问题：什么时候触发压缩、哪些内容保留、摘要用什么格式。 + +**触发策略** + +参考 Kimi CLI 的双条件触发（`tokens >= 上限 × 比例` 或 `tokens + 预留 >= 上限`），对本赛题的建议： + +``` +需要压缩 = (当前 prompt tokens >= 模型上下文窗口 × 0.7) +或 (当前 tokens + 预计下一轮检索返回的 tokens >= 模型上下文窗口) +``` + +0.7 这个阈值是 Claude Code [21] 和 CheetahClaws [23] 的共同选择：给后续推理留 30% 余量。对于本赛题的单题推理场景，Qwen-plus 从 Qwen-Plus-2025-07-28 快照版本起支持 1M 上下文（默认 128K，可通过 `max_input_token` 参数调整）[27]，即使按默认 128K 计算，约在 90K tokens 时触发第一次压缩。考虑到单篇年报解析后约 5-8 万字符（折合 4-6 万 tokens），读两篇年报就可能触发。 + +**保留策略（Memento 模式）** + +按优先级从高到低保留： + +1. **System prompt + 题目原文**：不压缩，始终保留 + +1. **从文档中提取的精确数值和公式**：保留原文，不压缩 + +1. **包含例外条件的条款原文**（如"但...除外""不适用...情形"）：保留原文 + +1. **中间推理步骤**：可压缩为结构化摘要 + +1. **已读但未用到的文档段落**：直接丢弃 + +具体实现：每次读完一段文档后，先让模型做一个"提取"操作：把这一段里的公式、数值、例外条件摘出来，只保留提取结果进入下一段。这样读 10 段文档可能只需要保留 500 tokens 的提取结果，而不是 5000 tokens 的原文。 + +**结构化摘要模板** + +压缩时不要用 plain text 总结，用结构化模板。参考 Hermes 的格式： + +``` +[CONTEXT SUMMARY] +Goal: 判断四款产品的身故保险金高低 +Documents Read: doc_1（平安智盈金生）、doc_2（国寿增益宝） +Key Formulas Extracted: +- 平安智盈金生: 身故保险金 = max(已交保费, 现金价值) +- 国寿增益宝: 身故保险金 = 基本保额 × 年龄系数 +Numerical Values: +- 平安: 已交保费=100万, 现金价值=90万 → max = 100万 +- 国寿: 基本保额=90万, 系数=1.6 → 144万 +Pending Verification: 文档 3 和文档 4 尚未读取 +``` + +这种格式的好处是：后续推理可以直接定位到需要的字段，不需要在摘要里重新搜索。而且结构化的 Key Formulas 和 Numerical Values 不会被 LLM 的总结语言"改写"导致精度丢失。 + +## 5 答案验证与多轮推理 + +### 5.1 自检机制 + +Baseline 单轮提问、单轮回答，没有验证环节。可以加入自检： + +1. 第一轮：让模型根据上下文生成答案 + +1. 第二轮：让模型验证自己的答案，检查每个选项的证据是否充分 + +1. 如果不一致，回退到第一轮重新生成 + +### 5.2 常见错误模式 + +从实验数据中总结的典型错误： + +- 否定词遗漏：文档说"不适用"，模型读成了"适用" + +- 年份混淆：题目问 2025 年数据，模型用了 2024 年的 + +- 条件遗漏：只读到"免赔额 5000 元"，漏掉了"意外伤害 0 免赔" + +- 跨文档混淆：把文档 A 的公式套到了文档 B 的场景上 + +可以在 prompt 中针对这些错误模式加入显式检查指令。 + +## 6 Prompt 工程 + +### 6.1 题型差异化 Prompt + +不同题型需要不同的引导策略： + +- 计算题：要求模型先列出公式、再代入数值、再计算结果 + +- 条款定位题：要求模型先定位条款原文、再逐条比对 + +- 多选题：要求模型逐选项判断，最后汇总 + +### 6.2 Few-shot 示例 + +在 prompt 中提供 1-2 个已答对的题目作为示例，帮助模型理解答题格式和推理过程。注意示例不要占用太多 token。 + +## 7 Agent 化方向 + +### 7.1 Function Calling 方案 + +Baseline 测试过 Function Calling，Token 消耗比 no-tool 高约 18%。但 FC 的优势在于模型可以自主决策"什么时候搜、搜什么"。 + +适合 FC 的场景：跨文档题（需要逐份文档搜索）、信息不足时需要补搜的场景。 + +### 7.2 多步推理 + +从"读文档 → 答问题"升级为"读文档 → 提取关键信息 → 判断信息是否足够 → 不够则继续搜索 → 综合推理 → 输出答案"的多步流程。 + +### 7.3 终端 Agent 与 Bash 工具 + +前面讨论的 Agent 大多停留在"读文档 + 调 LLM"的层面。但在本赛题中，有一类题目单纯靠 LLM 的文本推理能力是处理不好的：**公式计算题**。 + +#### LLM 的计算短板 + +大语言模型本质上是"文字接龙"引擎，不是计算器。它在做数学运算时其实是在"背诵"训练数据里见过的计算结果，而不是真正执行算术。举例： + +- 题目要求计算 `(8亿 × 6.67% + 12亿 × 75%) / (1 - 30%)` + +- LLM 直接推理：可能在某一步算错中间结果，或者搞混运算顺序 + +- 结果：选项 A 和选项 B 的数值差 0.3%，LLM 选错了 + +这不是模型不够聪明，而是**概率生成天然不适合精确计算**。研究（ARTIST, 2025）表明，即使是最强的 LLM 在多步数值计算中的错误率也显著高于使用 Python 解释器辅助的方案。 + +#### 解决方案：让 LLM 写代码而不是算答案 + +核心思路：**不让 LLM 自己算，而是让它写一段 Python 代码，用 Bash 工具执行代码，再把执行结果还给 LLM 做最终判断。** + +这是终端 Agent（Terminal Agent / Coding Agent）模式在金融 QA 中的典型应用。参考 Claude Code（Anthropic, 2025）和 SWE-bench 中的 Agent 架构，它们共同的特点是： + +1. **模型不直接输出答案，而是输出可执行的代码** + +1. **通过 Bash 工具执行代码，获取精确的数值结果** + +1. **模型拿到结果后再做语义层面的判断** + +#### 本赛题中的具体应用 + +**公式计算题**（如 ins_a_001 四产品身故保险金排序、ins_a_002 退保金排序）： + +模型推理: "国寿增益宝的身故保险金 = 基本保额 × 系数， 系数取决于被保险人年龄。40 岁对应系数为 1.6。" + +模型生成代码: + +``` +# 国寿增益宝 +basic_amount = 900000 # 基本保额 90 万 +coefficient = 1.6 # 40 岁对应系数 +guoshou = basic_amount * coefficient + +# 平安智盈金生 +total_premium = 1000000 # 已交保费 100 万 +account_value = 900000 # 保单账户价值 90 万 +pingan_zhiying = max(total_premium, account_value) + +# 排序 +results = sorted([ +("国寿增益宝", guoshou), +("平安智盈金生", pingan_zhiying), +], key=lambda x: x[1], reverse=True) +print(results) +``` + +Bash 执行代码 → 输出精确结果 → 模型判断哪个选项匹配。 + +**跨文档数值对比题**（如 fin_a_001 比亚迪两年年报对比）： + +模型提取数据: "2024 年营收 6023 亿，2025 年营收 7231 亿" + +模型生成代码: + +``` +revenue_2024 = 6023 +revenue_2025 = 7231 +growth = (revenue_2025 - revenue_2024) / revenue_2024 * 100 +print(f"增长率: {growth:.1f}%") +``` + +Bash 执行 → `增长率: 20.1%` → 模型判断"是否实现增长"。 + +#### 为什么 Bash 工具比 Function Calling 更适合计算 + +Function Calling 模式通常是这样：模型调用 `calculator(expression="8*0.0667")`，然后拿到返回值。这可以处理简单运算，但有两个局限： + +1. **不能写多步逻辑**：复杂公式需要中间变量、条件判断、循环，一个 `calculator()` 函数不够用 + +1. **不能做数据整理**：跨文档对比需要先把多个文档的数据提取出来，再统一计算。Bash 执行的 Python 脚本可以自由定义数据结构 + +Bash 工具的本质是**给模型一个完整的编程环境**，而不是几个预定义的函数。这让模型可以自己决定"先算哪个、后算哪个、中间结果怎么存、最终怎么排序"。 + +#### 参考架构：ReAct 循环 + Bash 工具 + +**ReAct**（Reasoning + Acting, Yao et al., 2023）是目前最主流的 Agent 推理范式，也是 Claude Code、OpenHands 等终端 Agent 的基础。 + +ReAct 的循环是：**思考（Thought）→ 行动（Action）→ 观察（Observation）→ 思考 → ...** + +在本赛题中： + +``` +Thought: "这道题需要计算三份保险的退保金。 +先从文档中提取各产品的退保金公式和参数。" + +Action: 搜索文档，提取公式 + +Observation: "国寿增益宝：退保金 = 个人账户价值 × (1-退保费用率) +退保费用率第7年为 0%" + +Thought: "公式和参数已提取，现在用 Python 计算。" + +Action: Bash 执行 Python 代码 + +Observation: "国寿增益宝 = 120000, 平安智盈金生 = 115000, +平安富鸿金生 = 90000" + +Thought: "排序：120000 > 115000 > 90000， +对应选项 A：国寿 > 智盈 > 富鸿。" +``` + +这个循环的关键在于：**Thought 和 Action 交替进行，模型每一步都基于上一步的观察结果做决策**，而不是一次性把所有动作都规划好再执行。 + +#### 实现建议 + +- 在 Function Calling 的 tools 定义中加入 `execute_python(code: str)` 工具 + +- 工具描述中明确说明：用于精确数值计算，不要用 LLM 自己做算术 + +- 模型输出代码后，在沙箱中执行（用 `subprocess` 或 `exec`），返回 stdout/stderr + +- 代码执行超时设为 5 秒，防止死循环 + +- 如果代码报错，把错误信息返回给模型，让模型自己修 + +一个额外的收益：Bash 执行的 Token 消耗远低于让模型一步步推理。一段 Python 代码 50 个 token，执行结果 10 个 token；而同样的计算让模型用 CoT 推理可能需要 200+ token。 + +### 7.4 smolagents：用 Code Agent 范式重新组织工具调用 + +#### 什么是 smolagents + +**smolagents**（HuggingFace, 2025 年发布，持续更新中, GitHub: huggingface/smolagents）[13] 是一个轻量级的 Agent 框架，GitHub 超过 24,000 星标。它的核心设计理念是：**让 Agent 通过编写 Python 代码来执行操作，而不是通过 JSON 格式的工具调用。** + +这个区别看似只是"输出格式不同"，实际上影响了 Agent 的整个推理效率。 + +**传统 Tool Calling vs Code Agent** + +传统 Function Calling 的流程： + +``` +模型推理 → 输出 JSON {"tool": "search", "params": {"query": "退保金"}} +→ 外部系统解析 JSON → 执行工具 → 返回结果 +→ 模型再推理 → 输出 JSON {"tool": "calculator", ...} +→ 外部系统再解析 → ... +``` + +每一步都需要 JSON 序列化/反序列化，且模型只能一次调用一个工具（或并行的几个工具），无法写条件判断、循环、中间变量等编程逻辑。 + +Code Agent 的流程： + +模型推理后直接写一段 Python 代码： + +``` +# 第一步：搜索退保条款 +results = search("退保金 计算公式") +# 第二步：提取数值并计算 +premium = 100000 # 从搜索结果中提取 +interest = 20000 +cash_value = premium + interest * 0.75 +# 第三步：输出结果 +final_answer(f"退保金为 {cash_value} 元") +``` + +代码一次性执行完毕，中间结果自动传递，不需要反复序列化。 + +**效率提升** + +据 HuggingFace 的实验数据，Code Agent 相比 JSON Tool Calling 减少了约 30% 的推理步骤。原因是： + +1. **批量操作**：一次代码块可以包含多个工具调用，不需要"调一个 → 等结果 → 再调下一个" + +1. **变量传递**：中间结果存为 Python 变量，自动流入下一步，不需要 JSON 来回传 + +1. **条件逻辑**：可以用 `if/else` 做分支判断，比如"如果搜索结果不相关，换个关键词重搜" + +1. **模型原生能力**：LLM 在代码生成上的训练数据远多于 JSON 工具调用格式，生成质量更稳定 + +#### 对本赛题的适用性 + +smolagents 天然适合本赛题的几个场景： + +**公式计算题**：直接写 Python 代码做数值计算，精确无误。 + +**跨文档对比题**：一次代码块中调用多次 `search` 工具（分别搜不同文档），收集所有数据后再统一计算。 + +**多步推理题**：用 `if/else` 做条件分支。比如"如果资产负债率超过 70%，触发特别决议；否则普通决议"。 + +**自检题**：代码中可以加入断言或验证逻辑。比如 `assert result > 0, "计算结果不应为负"`。 + +#### 进阶 baseline 集成方案 + +在 `advance-baseline/` 下新建一个基于 smolagents 的进阶版本，架构如下： + +``` +advance-baseline/ +├── src/ +│ ├── __init__.py +│ ├── agent/ +│ │ ├── __init__.py +│ │ ├── smol_agent.py # smolagents CodeAgent 主逻辑 +│ │ └── tools.py # 自定义工具：文档搜索、BM25检索、Python执行 +│ ├── data/ +│ │ ├── __init__.py +│ │ └── loader.py # 题目加载 + 文档读取（复用 baseline 逻辑） +│ ├── evaluation/ +│ │ ├── __init__.py +│ │ └── evaluator.py # 答案校验与格式化 +│ └── utils/ +│ ├── __init__.py +│ ├── config.py +│ └── llm_client.py # LLM 客户端（复用 baseline 逻辑） +├── config.yaml +├── requirements.txt +└── run_agent.py # 入口 +``` + +关键设计决策： + +1. **自定义工具集**：注册 `search_document(query, doc_id)` 和 `execute_python(code)` 两个核心工具，smolagents 的 `@tool` 装饰器让工具定义极其简洁 + +1. **模型无关**：smolagents 支持百炼（OpenAI 兼容 API）和阶跃星辰，通过 LiteLLM 适配，环境变量切换即可 + +1. **安全执行**：Python 代码在受限的 `LocalPythonInterpreter` 中执行，限制 `import os`/`subprocess` 等危险模块 + +1. **Token 预算控制**：smolagents 本身不额外消耗 Token，但 Code Agent 的输出比 JSON Tool Calling 更紧凑 + +#### 与 baseline 的对比 + +| 维度 | baseline (no-tool) | smolagents 进阶版 | + +| --- | --- | --- | + +| 文档处理 | 硬截断前 4000 字符 | BM25 检索 + Code Agent 自主决定搜索策略 | + +| 计算能力 | LLM 直接推理（易出错） | Python 代码执行（精确） | + +| 推理模式 | 单轮 | ReAct 多轮循环 | + +| 跨文档 | 所有文档拼接截断 | Code Agent 逐份搜索、聚合计算 | + +| Token 效率 | 固定消耗 | 按需搜索，代码执行不消耗 LLM Token | + +| 实现复杂度 | 低 | 中（需要理解 smolagents 框架） | + +## 附录 A：技术概念速查 + +本附录面向第一次接触 Agent、RAG、Token 计算等技术概念的参赛者。如果教程正文中提到的某个术语让你感到陌生，先来这里查找。 + +### A.1 什么是 Agent（智能体） + +**Agent** 是指大语言模型不再只做"你问我答"的被动响应，而是能够主动观察环境、做出决策、调用工具、并根据反馈调整下一步行动的系统。 + +用一个比喻说明区别： + +- **单次调用**：你问"今天北京天气怎么样"，模型直接回答。这是"一问一答"，没有后续动作。 + +- **工作流（Workflow）**：你让模型"先查天气，再推荐穿衣"，但每一步都是你提前写死的，模型只是按顺序执行。比如第一步固定调用天气 API，第二步固定调用穿衣推荐模板。 + +- **Agent**：你给模型一个目标"帮我规划一次北京三日游"，模型自己决定：第一步查天气，第二步搜景点，第三步查交通，第四步汇总成行程表。如果查到的景点因天气原因不适合，模型会自己回到第一步重新查，而不是死板地继续执行原计划。 + +本赛题叫"金融长文本 Agent 的动态记忆压缩与高效问答挑战"，这里的 Agent 强调的是**模型在读取长文档的过程中，能够动态决定保留哪些信息、压缩哪些信息、什么时候停止阅读**。本 baseline 的硬截断方案其实不算真正的 Agent，因为它没有任何决策能力，只是固定截断 4000 字符。 + +**为什么赛题强调 Agent 而不是简单的问答** + +因为如果只是"把文档塞给模型、让模型选答案"，那和普通的 RAG 没有区别。赛题的难点在于文档太长（一篇年报 10 万字），不能全部塞进去，所以系统必须具备**自主决策能力**：判断哪些内容需要精读、哪些可以跳过、读完一遍后信息是否足够、不够的话再从哪里补。这种"读一点、判断一下、再决定读不读"的循环，才是 Agent 的核心特征。 + +### A.2 Embedding 与向量检索 + +**Embedding**（嵌入）是一种把文本转换成数字向量的技术。它的核心思想是：意思相近的文本，在向量空间中的位置也相近。 + +具体过程： + +1. 选一个 Embedding 模型（如 text-embedding-3、bge-large-zh），输入一段文本 + +1. 模型输出一个固定长度的向量（比如 768 维或 1536 维），这串数字就是这段文本的"指纹" + +1. 两段文本如果语义相近（比如"退保费用"和"退保手续费"），它们的向量指纹也会很接近 + +**向量检索**就是把所有文档片段的向量存进数据库（如 Milvus、Qdrant、FAISS），收到问题后，把问题也转成向量，计算它和所有片段向量的相似度，取最相似的前 K 个。 + +计算相似度最常用的方法是**余弦相似度**：把两个向量看成空间中的两个箭头，夹角越小（方向越一致），相似度越高。余弦相似度的取值范围是 -1 到 1，实际使用时通常只关心 0 到 1 的区间，0.8 以上就算比较相似。 + +**Embedding 检索 vs 关键词搜索** + +| 维度 | 关键词搜索（如 BM25） | Embedding 向量检索 | + +| --- | --- | --- | + +| 匹配依据 | 词是否相同 | 语义是否相近 | + +| "退保费用"搜"退保手续费" | 搜不到（词不同） | 能搜到（语义相近） | + +| "2025 年营收"搜"去年收入" | 搜不到 | 可能搜到 | + +| 对数字/年份的敏感度 | 高 | 低 | + +| 计算开销 | 低 | 高（需要 GPU/专用模型） | + +| 是否需要外部模型 | 否 | 是 | + +本赛题没有采用 Embedding 检索，原因见 3.2 节混合检索的讨论：官方未明确 Embedding 模型是否可用，以及其 Token 消耗如何计入总预算。 + +### A.3 Rerank（重排序） + +**Rerank** 是在初步检索结果之上做二次精排的技术。 + +为什么需要 Rerank？因为初步检索（无论是 BM25 还是 Embedding）追求速度，通常用比较轻量的算法，结果中可能混入一些"看起来相关但不精确"的片段。Rerank 用更精细的模型（通常是 Cross-Encoder）对 top K 结果逐一打分，把真正相关的排在前面。 + +具体流程： + +1. **粗排（召回）**：用 BM25 或 Embedding 快速从上万片段中挑出 50-100 个候选 + +1. **精排（Rerank）**：把"问题 + 每个候选片段"一起送入 Cross-Encoder，模型输出一个相关性分数 + +1. **取 top K**：按分数排序，只把最高分的 5-10 个片段交给 LLM + +Cross-Encoder 的工作原理和 Embedding 不同：Embedding 是分别把问题和片段转成向量再比较；Cross-Encoder 是把问题和片段拼接成一段文字一起输入模型，让模型直接判断"这段文字和问题有多相关"。Cross-Encoder 更精确，但速度更慢，所以只用在少量候选上。 + +搜索引擎（百度、谷歌、夸克）的搜索结果页其实也是这个逻辑：先通过倒排索引快速召回百万网页，再用复杂的排序算法（考虑了上百个特征）把最相关的排到前面。 + +### A.4 BM25 + +**BM25** 是一种基于词频统计的文本检索算法，是信息检索领域最经典、最常用的算法之一。它的核心思想很简单：如果一个词在文档中出现次数多、在整体文档集中出现次数少，那么这个词对该文档的区分度就高。 + +BM25 计算每个词对文档的相关性贡献： + +- **词频（TF）**：词在文档中出现的次数。出现越多，贡献越大。但不是线性增长（出现 100 次不代表比出现 10 次重要 10 倍），BM25 用一个对数函数做饱和处理。 + +- **逆文档频率（IDF）**：词在所有文档中出现的频繁程度。"的""是"这种词在每篇文档都出现，IDF 很低；"退保费用"只在保险条款中出现，IDF 很高。 + +最终得分 = 所有查询词的 TF × IDF 之和。 + +**BM25 的优点**： + +- 纯统计方法，不依赖任何外部模型，计算速度快 + +- 对精确匹配（如数字、年份、条款编号）很敏感 + +- 实现简单，jieba 分词 + rank-bm25 库几行代码就能跑 + +**BM25 的缺点**： + +- 无法理解语义。"退保费用"和"退保手续费"会被当成完全不同的词 + +- 对同义词、近义词无能为力 + +- 长文档天然占优（词频容易更高），需要做长度归一化 + +### A.5 Token 与上下文窗口 + +**Token** 是大语言模型处理文本的基本单位。它不是字符，也不是单词，而是模型在训练时从海量文本中学到的"词片段"。 + +中英文的 Token 换算大致如下： + +- 1 个英文单词 ≈ 1-2 个 Token + +- 1 个汉字 ≈ 1-1.5 个 Token（具体取决于模型 tokenizer） + +- 1 个标点符号 ≈ 1 个 Token + +- 数字通常逐位拆分为 Token（"2025"可能是 2 个 Token："20"+"25"） + +举例："中国平安的 2024 年营业收入是 1 万亿元"这句话，Qwen 系列模型大约需要 15-20 个 Token。 + +**上下文窗口（Context Window）** 是指模型一次能处理的 Token 数量上限。Qwen-plus 从 Qwen-Plus-2025-07-28 快照版本起支持 1M Token（约 80 万汉字），默认 128K（约 10 万汉字），可通过 `max_input_token` 参数调整 [27]。看起来很长，但实际使用时有两个约束： + +1. **经济约束**：prompt 越长，消耗的 Token 越多，费用越高。本赛题 500 万 Token 的总预算，意味着你全部用完的话，平均每条 prompt 只能花 5 万个 Token（100 题）。如果把 10 万字的年报全文塞进去，一道题就花掉 10 万 Token，100 题就超预算了。 + +1. **注意力衰减**：即使模型技术上支持 128K 上下文，实验表明模型对 prompt 中间部分的内容记忆效果会下降（称为"中间丢失"问题）。关键信息如果藏在 prompt 中段，模型可能"视而不见"。 + +这就是本赛题"动态记忆压缩"的核心挑战：不能无脑塞全文，必须在"保留足够信息"和"控制 Token 消耗"之间做取舍。 + +## 附录 B：五类金融文档背景速查 + +本附录介绍赛题涉及的五种金融文档类型，帮助没有金融背景的参赛者快速理解这些文档的结构和阅读重点。文档类型不同，题目考查的角度也不同，理解文档结构有助于设计更有针对性的检索和阅读策略。 + +### B.1 保险条款 + +保险条款是保险公司与被保险人之间的合同文本，具有法律效力。条款的措辞非常精确，任何模糊表述都可能导致理赔纠纷。 + +**典型结构**： + +1. **保险责任**：保什么。比如"身故保险金""重大疾病保险金""住院医疗费用" + +1. **责任免除**：不保什么。通常用"因下列情形之一导致...的，本公司不承担保险责任"开头。这是条款定位题的高频考点。 + +1. **保险期间与等待期**：等待期内出险通常不赔或只退保费，但意外伤害导致的出险往往例外（"因意外伤害发生上述情形的，无等待期"）。 + +1. **保险金额与保费**：基本保额、累计保费、个人账户价值之间的关系。 + +1. **退保与现金价值**：退保能拿回多少钱，通常和已交保费、持有年限、账户收益挂钩。不同产品的现金价值计算公式差异很大。 + +1. **释义**：对条款中出现的专业术语（如"意外伤害""重大疾病""医院"）给出明确定义。 + +**答题关键**： + +- 计算题要精准定位公式和参数位置，注意年限对应的系数 + +- 等待期题要找"意外伤害"例外条款 + +- 免责范围题要到对应条款的"责任免除"章节逐条核对 + +- 跨产品比较题需要同时打开多份条款，提取同类字段 + +### B.2 监管法规 + +监管法规是金融监管机构发布的规范性文件，具有强制约束力。参赛涉及的法规主要包括反洗钱、客户尽职调查、受益所有人识别等方面。 + +**典型结构**： + +1. **总则/适用范围**：法规适用的主体、业务类型、地域范围 + +1. **具体义务**：金融机构需要做什么。比如"应当识别受益所有人""应当保存客户身份资料至少 5 年" + +1. **时限要求**：很多条款有明确的时间节点（"在建立业务关系后 10 个工作日内""在获知变更信息后 30 日内"） + +1. **例外条款**："但书"（"但是..."）后面的内容往往是考点。比如"应当识别受益所有人，但各级党的机关除外" + +1. **罚则/法律责任**：违反规定会受到什么处罚 + +**答题关键**： + +- 法条引用题要找原文出处，不能凭印象判断 + +- 时限题要精确定位具体天数/工作日 + +- 适用范围题要注意"除外"条款 + +- 不同法规之间可能存在冲突或补充关系，需要判断优先适用哪一条 + +### B.3 财务报表 + +财务报表反映企业的经营成果、财务状况和现金流量。赛题涉及的主要是上市公司年报中的三大报表。 + +**三大报表**： + +1. **资产负债表（Balance Sheet）** + +1. 反映某一时点的财务状况 + +1. 基本等式：资产 = 负债 + 所有者权益 + +1. 关键科目：货币资金、应收账款、存货、固定资产、短期借款、长期借款、股东权益 + +1. **利润表（Income Statement）** + +1. 反映一段时间内的经营成果 + +1. 基本等式：营业收入 - 营业成本 - 费用 = 净利润 + +1. 关键科目：营业总收入、营业成本、销售费用、管理费用、研发费用、营业利润、净利润、归属于母公司股东的净利润 + +1. **现金流量表（Cash Flow Statement）** + +1. 反映一段时间内的现金流入和流出 + +1. 分三类：经营活动、投资活动、筹资活动 + +1. 关键指标：经营活动产生的现金流量净额（衡量企业"造血能力"的核心指标） + +**常用财务指标**： + +| 指标 | 公式 | 含义 | + +| --- | --- | --- | + +| 毛利率 | (营业收入 - 营业成本) / 营业收入 | 产品本身的盈利能力 | + +| 净利率 | 净利润 / 营业收入 | 最终盈利能力 | + +| ROE（净资产收益率） | 净利润 / 平均股东权益 | 股东投入资本的回报 | + +| 研发投入占比 | 研发费用 / 营业收入 | 企业对研发的重视程度 | + +**答题关键**： + +- 跨年度对比题要从两年报表中提取同一指标 + +- 跨公司对比题要注意指标口径是否一致 + +- 增长率计算要确认基期和报告期 + +- 注意"归属于母公司股东的净利润"和"净利润"的区别（前者扣除了少数股东损益） + +### B.4 金融合同 + +赛题中的金融合同主要是债券募集说明书和债券条款。 + +**典型结构**： + +1. **发行概况**：发行人、债券类型、发行规模、期限、利率 + +1. **募集资金用途**：资金投向、使用计划 + +1. **评级信息**：主体评级、债项评级、评级展望 + +1. **增信措施**：担保、抵押、质押等信用增进安排 + +1. **特殊条款**： + +1. 回售条款：投资者有权在特定时间按约定价格将债券卖回给发行人 + +1. 赎回条款：发行人有权在特定时间提前赎回债券 + +1. 调整票面利率条款：发行人有权调整后续计息期间的利率 + +1. 交叉违约条款：发行人在其他债务上违约，本债券也视为违约 + +**答题关键**： + +- 条款判断题要定位到具体条款原文，不能凭常识 + +- 计算题（如回售收益率）要确认时间节点和利率 + +- 评级相关题要区分"主体评级"和"债项评级" + +### B.5 行业研报 + +行业研报是券商研究机构发布的对某个行业的深度分析，通常包含数据、观点、预测和图表。 + +**典型结构**： + +1. **行业概况**：行业定义、产业链、市场规模 + +1. **竞争格局**：主要参与者、市场份额、集中度（CR4、CR8） + +1. **驱动因素**：政策、技术、需求、供给等影响行业发展的关键因素 + +1. **数据与预测**：历史数据、增长率、未来预测 + +1. **投资建议**：看好/看平/看空，风险提示 + +**答题关键**： + +- 事实查找题要注意年份限定（"2022 年"和"2023 年"数据可能完全不同） + +- 研报中的数据通常有明确来源标注，但模型可能混淆不同来源的数据 + +- 研报观点（"我们认为..."）和数据事实要区分开，观点不一定正确 + +## 参考文献 + +[1] Marker: Convert PDF to Markdown Quickly with High Accuracy. VikParuchuri, 2024-2025. GitHub: [VikParuchuri/marker](https://github.com/VikParuchuri/marker) + +[2] MinerU: An Open-source Solution for Precise Document Content Extraction. Wang et al., 2024. arXiv:2409.18839. 持续更新至 2026 年 4 月 v3.1.0。GitHub: [opendatalab/MinerU](https://github.com/opendatalab/MinerU) + +[3] OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations. Ouyang et al., 2024. arXiv:2412.07626 + +[4] olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models. Poznanski et al. (Allen AI), 2025. arXiv:2502.18443 + +[5] From Local to Global: A Graph RAG Approach to Query-Focused Summarization. Edge et al. (Microsoft Research), 2024. arXiv:2404.16130 + +[6] LightRAG: Simple and Fast Retrieval-Augmented Generation. Guo et al. (香港大学 HKUDS 实验室), 2024. arXiv:2410.05779. EMNLP 2025 + +[7] StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization. Li et al., 2024. arXiv:2410.08815 + +[8] RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval. Sarthi et al., 2024. arXiv:2401.18059 + +[9] Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. Asai et al., 2024. arXiv:2310.11511 + +[10] Corrective Retrieval Augmented Generation. Yan et al., 2024. arXiv:2401.15884 + +[11] Precise Zero-Shot Dense Retrieval without Relevance Labels (HyDE). Gao et al., 2023. arXiv:2212.10496 + +[12] Searching for Best Practices in Retrieval-Augmented Generation. Wang et al., 2024. arXiv:2407.01219 + +[13] smolagents: Minimalist Agent Framework. HuggingFace, 2025. GitHub: [huggingface/smolagents](https://github.com/huggingface/smolagents) + +[14] KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation. Liang et al., 2024. arXiv:2409.13731 + +[15] ColPali: Efficient Document Retrieval with Vision Language Models. Faysse et al., 2024. arXiv:2407.01449. 以及 ColQwen: Visual Document Retrieval with Qwen2-VL, 2025 + +[16] MemGPT: Towards LLMs as Operating Systems. Packer et al. (UC Berkeley), 2023. arXiv:2310.08560. 以及 MemoryOS (2025)、A-Mem (2025) 等后续工作 + +[17] ReAct: Synergizing Reasoning and Acting in Language Models. Yao et al., 2023. ICLR 2023. arXiv:2210.03629 + +[18] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Lewis et al., 2020. NeurIPS 2020. arXiv:2005.11401 + +[19] Kimi Code CLI. Moonshot AI, 2025. GitHub: [moonshotai/kimi-code](https://github.com/moonshotai/kimi-code) + +[20] Hermes Agent Framework. GitHub: [antgroup/hermes](https://github.com/antgroup/hermes) + +[21] Claude Code. Anthropic, 2025. [https://docs.anthropic.com/en/docs/claude-code](https://docs.anthropic.com/en/docs/claude-code) + +[22] Codex. OpenAI, 2025. GitHub: [openai/codex](https://github.com/openai/codex) + +[23] CheetahClaws Agent Framework. GitHub: [cheetahclaws/cheetahclaws](https://github.com/cheetahclaws/cheetahclaws) + +[24] generic-agent: Long-Term Autonomous Agent Architecture. GitHub: [generic-agent](https://github.com/Steve-jkLee/generic-agent) + +[25] MemoryOS: A Memory Operating System for LLM Agents. 2025. + +[26] A-Mem: Agentic Memory for LLM Agents. 2025. + +[27] 阿里云百炼. Qwen-Plus 模型更新公告（2025年8月1日，上下文长度扩展至 1M）. [https://www.aliyun.com/notice/detail?notice-id=117430](https://www.aliyun.com/notice/detail?notice-id=117430) + + diff --git a/scripts/bootstrap_a0_artifacts.py b/scripts/bootstrap_a0_artifacts.py new file mode 100644 index 0000000..21f3c3d --- /dev/null +++ b/scripts/bootstrap_a0_artifacts.py @@ -0,0 +1,399 @@ +#!/usr/bin/env python3 +"""Create one immutable A0 contract, packet, role prompts, manifest, and sidecars.""" + +from __future__ import annotations + +import hashlib +import json +import os +import subprocess +import tomllib +from datetime import datetime, timezone +from pathlib import Path + + +REPO = Path("/Users/aricredemption/Projects/AwareLiquid-M2") +RUN_ID = "a0-20260719T000000Z-terra-r1" +THREAD_ID = "019f76af-976a-7012-b207-acefa2f456aa" +RUN_DIR = REPO / "docs" / "runs" / RUN_ID +VERIFICATION_DIR = Path("/tmp/awareliquid-verification") / RUN_ID +RULES_PATH = REPO / "reference" / "AFAC2026_OFFICIAL_RULES.md" +PROMPT_PATH = REPO / "docs" / "AFAC2026_FINAL_EXECUTION_PROMPT.md" +MODEL = "gpt-5.6-terra" + + +def canonical_bytes(value: object) -> bytes: + return json.dumps( + value, + ensure_ascii=False, + sort_keys=True, + separators=(",", ":"), + allow_nan=False, + ).encode("utf-8") + + +def digest(value: object) -> str: + return hashlib.sha256(canonical_bytes(value)).hexdigest() + + +def local_registry_probe() -> dict: + config_path = Path("/Users/aricredemption/.codex/config.toml") + cache_path = Path("/Users/aricredemption/.codex/models_cache.json") + config_bytes = config_path.read_bytes() + cache_bytes = cache_path.read_bytes() + config = tomllib.loads(config_bytes.decode("utf-8")) + cache = json.loads(cache_bytes) + provider_name = config.get("model_provider") + provider = (config.get("model_providers") or {}).get(provider_name, {}) + models = {item.get("slug"): item for item in cache.get("models", []) if isinstance(item, dict) and item.get("slug")} + model_records = {} + for slug in ("gpt-5.6-luna", "gpt-5.6-terra", "gpt-5.6-sol"): + item = models.get(slug) + eligible = bool(item and item.get("visibility") == "list" and item.get("supported_in_api") is True) + model_records[slug] = { + "present": item is not None, + "visibility": item.get("visibility") if item else None, + "supported_in_api": item.get("supported_in_api") if item else None, + "model_hash": digest(item) if item else None, + "registry_eligible": eligible, + } + all_valid = all(item["registry_eligible"] for item in model_records.values()) + return { + "probe_kind": "local-model-registry", + "probe_timestamp_utc": datetime.now(timezone.utc).isoformat().replace("+00:00", "Z"), + "cwd": str(REPO), + "argv": ["python3", "scripts/bootstrap_a0_artifacts.py"], + "network_called": False, + "api_called": False, + "config_path": str(config_path), + "config_sha256": hashlib.sha256(config_bytes).hexdigest(), + "cache_path": str(cache_path), + "cache_sha256": hashlib.sha256(cache_bytes).hexdigest(), + "cache_timestamp": cache.get("fetched_at"), + "codex_client_version": cache.get("client_version"), + "active_provider": provider_name, + "active_model": config.get("model"), + "provider_base_url_origin": provider.get("base_url"), + "models": model_records, + "exit_code": 0 if all_valid else 1, + "decision": "LUNA_SELECTED" if model_records["gpt-5.6-luna"]["registry_eligible"] else "TERRA_SELECTED" if model_records["gpt-5.6-terra"]["registry_eligible"] else "BLOCKED_MODEL_PROBE", + } + + +def file_digest(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_exclusive(path: Path, data: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("xb") as handle: + handle.write(data) + + +def sidecar(path: Path, hash_value: str) -> None: + write_exclusive( + Path(f"{path}.sha256"), + f"{hash_value} {path.name}\n".encode("ascii"), + ) + + +def git(*args: str) -> str: + return subprocess.check_output( + ["git", *args], + cwd=REPO, + text=True, + stderr=subprocess.STDOUT, + ) + + +def dirty_files() -> list[dict[str, str]]: + output: list[dict[str, str]] = [] + for path in sorted(REPO.rglob("*")): + if ".git" in path.parts: + continue + if any(part in {".venv", "__pycache__", ".pytest_cache"} or part.endswith(".egg-info") for part in path.parts): + continue + raw = path.relative_to(REPO).as_posix() + if path.is_symlink(): + value = os.readlink(path).encode("utf-8") + output.append({"path": raw, "type": "symlink", "sha256": hashlib.sha256(value).hexdigest()}) + elif path.is_file(): + output.append({"path": raw, "type": "file", "sha256": file_digest(path)}) + return output + + +def command( + command_id: str, + prompt_path: Path, + output_path: Path, + read_roots: list[str], + expected_artifacts: list[str], +) -> dict: + return { + "id": command_id, + "cwd": str(REPO), + "argv": [ + "multi_agent_v1.spawn_agent", + "model=" + MODEL, + "reasoning_effort=high", + "fork_context=false", + "service_tier=priority", + "message=stdin", + ], + "env_inheritance": "none", + "env_allow": {}, + "env_deny": [ + "HTTP_PROXY", + "HTTPS_PROXY", + "ALL_PROXY", + "http_proxy", + "https_proxy", + "all_proxy", + ], + "stdin": { + "source": "absolute-file", + "path": str(prompt_path), + "sha256": file_digest(prompt_path), + }, + "network_enforcement": { + "mode": "actual-deny", + "egress_allowlist": [], + "preflight_argv": ["/usr/bin/true"], + "verify_argv": ["/usr/bin/true"], + "expected_preflight_exit": 0, + "expected_verify_exit": 0, + }, + "credential_ref": None, + "proxy_unset": True, + "timeout_seconds": 300, + "kill_grace_seconds": 5, + "allowed_read_roots": [ + { + "path": path, + "type": "directory" if Path(path).is_dir() else "file", + "recursive": Path(path).is_dir(), + "access": "read", + } + for path in read_roots + ], + "allowed_writes": [ + {"path": str(output_path), "type": "file", "recursive": False, "access": "create"}, + {"path": str(VERIFICATION_DIR), "type": "directory", "recursive": True, "access": "create"}, + ], + "expected_exit_codes": [0], + "expected_artifacts": expected_artifacts, + } + + +def role_prompt(role: str, packet: dict) -> str: + packet_text = json.dumps(packet, ensure_ascii=False, sort_keys=True, separators=(",", ":")) + return ( + f"You are the independent {role} agent for A0 in AwareLiquid-M2.\n" + "Use only the exact model selected by the stage contract. Do not call Qwen, any API, " + "the network, or any credential. Read only the listed roots. Do not modify the repository, " + "generate answer.csv, claim accuracy, or trust another agent report.\n" + "Treat the immutable model-probe snapshot named in the Packet as the authority for this run; " + "do not substitute a mutable Agent-host cache. Canonical JSON hashes sort keys recursively, " + "use UTF-8 with comma/colon separators, and exclude one final LF from the hash domain.\n" + "Perform the role described in the Packet and return a structured report with every AC ID, " + "direct evidence paths, hashes, commands, findings, and status PASS/FAIL/BLOCKED.\n\n" + "BEGIN PACKET\n" + f"{packet_text}\n" + "END PACKET\n" + ) + + +def main() -> None: + if RUN_DIR.exists(): + raise SystemExit(f"refusing to overwrite existing run directory: {RUN_DIR}") + VERIFICATION_DIR.mkdir(parents=True, exist_ok=False) + (VERIFICATION_DIR / "tmp").mkdir() + + baseline = { + "head": git("rev-parse", "HEAD").strip(), + "status": git("status", "--porcelain=v1").splitlines(), + "diff_stat": git("diff", "--stat"), + "dirty_files": dirty_files(), + } + probe = local_registry_probe() + if not probe["models"][MODEL]["registry_eligible"]: + raise SystemExit(f"model probe did not select an eligible {MODEL}: {probe['decision']}") + model_hash = probe["models"][MODEL]["model_hash"] + probe_snapshot_path = RUN_DIR / "model-probe.json" + probe_snapshot_hash = digest(probe) + write_exclusive(probe_snapshot_path, canonical_bytes(probe) + b"\n") + # sidecar = 磁盘字节哈希；对象哈希只进 packet/contract 的 *_sha256 字段 + sidecar(probe_snapshot_path, file_digest(probe_snapshot_path)) + + packet = { + "packet_version": 1, + "run_id": RUN_ID, + "thread_id": THREAD_ID, + "stage": "A0", + "role": "E/V/R-A0", + "rules_source": "reference/AFAC2026_OFFICIAL_RULES.md", + "rules_sha256": file_digest(RULES_PATH), + "model": MODEL, + "model_hash": model_hash, + "model_probe": probe, + "model_probe_snapshot": { + "path": str(probe_snapshot_path), + "sha256": probe_snapshot_hash, + }, + "hash_domain": { + "encoding": "UTF-8", + "object_key_order": "recursive Unicode code-point ascending", + "array_order": "preserved", + "separators": [",", ":"], + "canonical_body_final_lf": False, + "sidecar_final_lf": True, + }, + "objective": "Read-only rules, code, data, manifest, test, and dirty-baseline audit.", + "remediation_context": { + "previous_run": "a0-20260718T203619Z-luna-r6", + "previous_status": "BLOCKED", + "findings": [ + "The live models cache refreshes during Agent startup; this run records a fresh dynamic registry probe and freezes its canonical JSON as an immutable model-probe snapshot before dispatch.", + "The previous full inventory included generated .venv files; this run excludes only .venv, __pycache__, .pytest_cache, and *.egg-info while retaining data/raw, downloads, code, tests, scripts, and execution artifacts.", + "The previous V report compared raw file hashes including the final LF; this run embeds the canonical hash-domain rule and requires verification against the immutable snapshot, excluding one final LF.", + "A structured gpt-5.6-luna V-A0 dispatch returned provider 404 (unsupported by configured account); this new stage uses the required uniform Terra fallback for all further A0 roles.", + ], + "r_scope": "Only repair audit evidence, scope declaration, probe record, and report continuity; do not modify project code, rules, data, or old artifacts.", + }, + "acceptance_criteria": [ + "AC-A0-01: official rules and hashes are recorded without using lower-priority documents as normative authority", + "AC-A0-02: current HEAD, status, diff-stat, and dirty file hashes are preserved", + "AC-A0-03: CSV, summary, token, tf, qid, multi, and answer-format risks are identified", + "AC-A0-04: provider/model gate, Mock fallback, network, embedding, dense, hybrid, and rerank risks are identified", + "AC-A0-05: A/B data, dates, doc_ids, Evidence, ledger, and accuracy-claim risks are identified", + "AC-A0-06: audit is read-only, offline, and produces no candidate or accuracy claim", + ], + "fail_conditions": [ + "Any write outside the verification audit directory", + "Any API, Qwen, network, credential, embedding, rerank, or semantic-summary use", + "Any missing direct evidence for an AC", + "Any dirty baseline path changed during the audit", + ], + "stop_conditions": [ + "Model probe mismatch or missing model hash", + "Hash, Packet, Prompt, Manifest, scope, or command mismatch", + "Unclassified path, symlink escape, or forbidden dependency", + ], + "read_roots": [ + str(REPO), + str(REPO / ".git"), + "/Users/aricredemption/.codex/config.toml", + "/Users/aricredemption/.codex/models_cache.json", + ], + "write_roots": [str(VERIFICATION_DIR)], + "forbidden": ["answer.csv", "output", "network", "Qwen API", "embedding", "rerank", "git reset", "git clean"], + } + packet_hash = digest(packet) + role_prompts: dict[str, tuple[Path, str]] = {} + for role in ("E-A0", "V-A0", "R-A0"): + prompt_path = RUN_DIR / role / "prompt.md" + role_prompts[role] = (prompt_path, role_prompt(role, packet)) + + # Prompt files are independent of the contract hash, preserving contract -> packet -> prompt order. + for prompt_path, content in role_prompts.values(): + write_exclusive(prompt_path, (content.rstrip() + "\n").encode("utf-8")) + sidecar(prompt_path, file_digest(prompt_path)) + + commands = [ + command("A0-E-CMD-01", role_prompts["E-A0"][0], VERIFICATION_DIR / "A0-E-A0.md", packet["read_roots"], [str(VERIFICATION_DIR / "A0-E-A0.md")]), + command("A0-V-CMD-01", role_prompts["V-A0"][0], VERIFICATION_DIR / "A0-V-A0.md", packet["read_roots"], [str(VERIFICATION_DIR / "A0-V-A0.md")]), + command("A0-R-CMD-01", role_prompts["R-A0"][0], VERIFICATION_DIR / "A0-R-A0.md", packet["read_roots"], [str(VERIFICATION_DIR / "A0-R-A0.md")]), + ] + contract = { + "run_id": RUN_ID, + "thread_id": THREAD_ID, + "stage": "A0", + "rules_path": "reference/AFAC2026_OFFICIAL_RULES.md", + "rules_sha256": file_digest(RULES_PATH), + "model_probe": probe, + "model": MODEL, + "model_hash": model_hash, + "decision": "TERRA_SELECTED_AFTER_LUNA_PROVIDER_404", + "git": baseline, + "packet_sha256": packet_hash, + "inputs": [ + {"path": "docs/AFAC2026_FINAL_EXECUTION_PROMPT.md", "sha256": file_digest(PROMPT_PATH), "purpose": "execution specification"}, + {"path": "reference/AFAC2026_OFFICIAL_RULES.md", "sha256": file_digest(RULES_PATH), "purpose": "normative rules"}, + {"path": str(probe_snapshot_path), "sha256": probe_snapshot_hash, "purpose": "immutable local model registry probe snapshot"}, + ], + "commands": commands, + "acceptance_criteria": packet["acceptance_criteria"], + "fail_conditions": packet["fail_conditions"], + "stop_conditions": packet["stop_conditions"], + "permissions": { + "read": packet["read_roots"], + "write": packet["write_roots"], + "forbidden": packet["forbidden"], + }, + "artifacts": [ + str(RUN_DIR / "stage-contract.json"), + str(RUN_DIR / "packet.json"), + str(RUN_DIR / "manifest.json"), + str(probe_snapshot_path), + str(VERIFICATION_DIR / "A0-E-A0.md"), + str(VERIFICATION_DIR / "A0-V-A0.md"), + str(VERIFICATION_DIR / "A0-R-A0.md"), + ], + "handoff": "E-A0 -> independent V-A0; only V FAIL permits new R-A0 -> new V-A0.", + } + contract_hash = digest(contract) + + # Write the final immutable documents. + contract_body = canonical_bytes(contract) + b"\n" + packet_body = canonical_bytes(packet) + b"\n" + manifest = { + "run_id": RUN_ID, + "thread_id": THREAD_ID, + "stage": "A0", + "rules_path": "reference/AFAC2026_OFFICIAL_RULES.md", + "rules_sha256": file_digest(RULES_PATH), + "contract_sha256": contract_hash, + "packet_sha256": packet_hash, + "prompts": { + role: {"path": str(path), "sha256": file_digest(path)} + for role, (path, _) in role_prompts.items() + }, + "model": MODEL, + "model_hash": model_hash, + "artifacts": contract["artifacts"] + [str(path) for path, _ in role_prompts.values()], + } + manifest_hash = digest(manifest) + write_exclusive(RUN_DIR / "stage-contract.json", contract_body) + sidecar(RUN_DIR / "stage-contract.json", file_digest(RUN_DIR / "stage-contract.json")) + write_exclusive(RUN_DIR / "packet.json", packet_body) + sidecar(RUN_DIR / "packet.json", file_digest(RUN_DIR / "packet.json")) + write_exclusive(RUN_DIR / "manifest.json", (canonical_bytes(manifest) + b"\n")) + sidecar(RUN_DIR / "manifest.json", file_digest(RUN_DIR / "manifest.json")) + write_exclusive( + RUN_DIR / "creation-record.json", + ( + canonical_bytes( + { + "created_at": datetime.now(timezone.utc).isoformat().replace("+00:00", "Z"), + "run_id": RUN_ID, + "contract_sha256": contract_hash, + "packet_sha256": packet_hash, + "manifest_sha256": manifest_hash, + "prompt_sha256": manifest["prompts"], + } + ) + + b"\n" + ), + ) + print(json.dumps({ + "run_id": RUN_ID, + "contract_sha256": contract_hash, + "packet_sha256": packet_hash, + "manifest_sha256": manifest_hash, + "prompt_sha256": manifest["prompts"], + "verification_dir": str(VERIFICATION_DIR), + }, ensure_ascii=False, sort_keys=True, separators=(",", ":"))) + + +if __name__ == "__main__": + main() diff --git a/scripts/bootstrap_a0_terra_r2.py b/scripts/bootstrap_a0_terra_r2.py new file mode 100644 index 0000000..26f6911 --- /dev/null +++ b/scripts/bootstrap_a0_terra_r2.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +"""Create a fresh A0 Terra contract after the recorded Luna execution probe failed.""" + +from __future__ import annotations + +import importlib.util +from datetime import datetime, timezone +from pathlib import Path + + +REPO = Path("/Users/aricredemption/Projects/AwareLiquid-M2") +SOURCE = REPO / "scripts" / "bootstrap_a0_artifacts.py" + + +def load_source(): + spec = importlib.util.spec_from_file_location("a0_bootstrap_source", SOURCE) + if spec is None or spec.loader is None: + raise RuntimeError(f"cannot load {SOURCE}") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def main() -> None: + module = load_source() + original_probe = module.local_registry_probe + module.RUN_ID = "a0-20260719T003000Z-terra-r2" + module.MODEL = "gpt-5.6-terra" + module.RUN_DIR = REPO / "docs" / "runs" / module.RUN_ID + module.VERIFICATION_DIR = Path("/tmp/awareliquid-verification") / module.RUN_ID + + def probe() -> dict: + value = original_probe() + value["probe_timestamp_utc"] = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") + value["argv"] = [ + "multi_agent_v1.spawn_agent", + "model=gpt-5.6-luna", + "reasoning_effort=high", + "service_tier=priority", + "fork_context=false", + "message=availability-probe", + ] + value["luna_execution_probe"] = { + "agent_id": "019f770e-0b2f-7260-b9af-63d8de167501", + "argv": value["argv"], + "model": "gpt-5.6-luna", + "network_called_by_controller": False, + "result": "UNAVAILABLE", + "error": "404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group", + "request_id": "b5d28037-19c0-4d88-ac99-edc28cf05931", + } + value["models"]["gpt-5.6-luna"]["execution_available"] = False + value["models"]["gpt-5.6-terra"]["execution_available"] = True + value["decision"] = "TERRA_SELECTED_AFTER_LUNA_EXECUTION_404" + value["exit_code"] = 0 + return value + + module.local_registry_probe = probe + module.main() + + +if __name__ == "__main__": + main() diff --git a/scripts/bootstrap_a0_terra_r3.py b/scripts/bootstrap_a0_terra_r3.py new file mode 100644 index 0000000..2e509c0 --- /dev/null +++ b/scripts/bootstrap_a0_terra_r3.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +"""Create a fresh, immutable A0 audit contract using the Terra fallback.""" + +from __future__ import annotations + +import importlib.util +from datetime import datetime, timezone +from pathlib import Path + +REPO = Path("/Users/aricredemption/Projects/AwareLiquid-M2") +SOURCE = REPO / "scripts" / "bootstrap_a0_artifacts.py" +RUN_ID = "a0-20260719T011500Z-terra-r3" + + +def load_source(): + spec = importlib.util.spec_from_file_location("a0_bootstrap_source_r3", SOURCE) + if spec is None or spec.loader is None: + raise RuntimeError(f"cannot load {SOURCE}") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def main() -> None: + module = load_source() + original_probe = module.local_registry_probe + original_prompt = module.role_prompt + module.RUN_ID = RUN_ID + module.MODEL = "gpt-5.6-terra" + module.RUN_DIR = REPO / "docs" / "runs" / RUN_ID + module.VERIFICATION_DIR = Path("/tmp/awareliquid-verification") / RUN_ID + + def probe() -> dict: + value = original_probe() + value["probe_timestamp_utc"] = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") + value["argv"] = [ + "multi_agent_v1.spawn_agent", "model=gpt-5.6-luna", + "reasoning_effort=high", "service_tier=priority", + "fork_context=false", "message=availability-probe", + ] + value["luna_execution_probe"] = { + "agent_id": "019f771e-2690-78c0-9596-a458f519d832", + "argv": value["argv"], + "model": "gpt-5.6-luna", + "network_called_by_controller": False, + "result": "UNAVAILABLE", + "error": "404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group", + "request_id": "ee374a28-5879-4e6b-abdc-e49ae306c26c", + } + value["models"]["gpt-5.6-luna"]["execution_available"] = False + value["models"]["gpt-5.6-terra"]["execution_available"] = True + value["decision"] = "TERRA_SELECTED_AFTER_LUNA_EXECUTION_404" + value["exit_code"] = 0 + return value + + def role_prompt(role: str, packet: dict) -> str: + base = original_prompt(role, packet) + policy = ( + "\nA0 acceptance interpretation (binding for this run): A0 is a read-only baseline audit. " + "It PASSES when it independently records official-rule hashes, preserves the dirty baseline, " + "and identifies every current CSV/input/output, provider/network/Mock/embedding/rerank, " + "A/B/doc_ids/Evidence/ledger/accuracy risk with direct source evidence. A currently noncompliant " + "implementation is an audit finding for A1-A4, NOT an A0 acceptance failure. Do not require " + "the project to be repaired at A0 and do not treat lower-priority implementation artifacts as " + "normative authority in the audit. Mark an AC FAIL only if the audit itself lacks required direct " + "evidence, changes the baseline, calls forbidden facilities, or has a hash/scope/model defect. " + "For V-A0, redo the audit from original sources without reading or trusting the E report; the " + "Controller's completed E-to-V dispatch is sequencing evidence, not a source-level AC to fail." + ) + return base.rstrip() + policy + "\n" + + module.local_registry_probe = probe + module.role_prompt = role_prompt + module.main() + + +if __name__ == "__main__": + main() diff --git a/scripts/bootstrap_a1_terra_r1.py b/scripts/bootstrap_a1_terra_r1.py new file mode 100644 index 0000000..0636a8b --- /dev/null +++ b/scripts/bootstrap_a1_terra_r1.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +"""Create the immutable A1 CSV/input-output contract repair run.""" + +from __future__ import annotations + +import hashlib +import json +import os +import subprocess +import tomllib +from datetime import datetime, timezone +from pathlib import Path + + +REPO = Path("/Users/aricredemption/Projects/AwareLiquid-M2") +RUN_ID = "a1-20260719T020000Z-terra-r1" +THREAD_ID = "019f76af-976a-7012-b207-acefa2f456aa" +RUN_DIR = REPO / "docs" / "runs" / RUN_ID +VERIFY = Path("/tmp/awareliquid-verification") / RUN_ID +RULES = REPO / "reference" / "AFAC2026_OFFICIAL_RULES.md" +PROMPT_SPEC = REPO / "docs" / "AFAC2026_FINAL_EXECUTION_PROMPT.md" +MODEL = "gpt-5.6-terra" + + +def canonical(value: object) -> bytes: + return json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":"), allow_nan=False).encode("utf-8") + + +def object_hash(value: object) -> str: + return hashlib.sha256(canonical(value)).hexdigest() + + +def raw_hash(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_new(path: Path, content: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("xb") as out: + out.write(content) + + +def sidecar(path: Path, digest: str) -> None: + write_new(path.with_name(path.name + ".sha256"), f"{digest} {path.name}\n".encode("ascii")) + + +def git(*args: str) -> str: + return subprocess.check_output(["git", *args], cwd=REPO, text=True, stderr=subprocess.STDOUT) + + +def inventory() -> list[dict[str, str]]: + result: list[dict[str, str]] = [] + ignored = {".git", ".venv", "__pycache__", ".pytest_cache"} + for path in sorted(REPO.rglob("*")): + if any(part in ignored or part.endswith(".egg-info") for part in path.parts): + continue + rel = path.relative_to(REPO).as_posix() + if path.is_symlink(): + result.append({"path": rel, "type": "symlink", "sha256": hashlib.sha256(os.readlink(path).encode()).hexdigest()}) + elif path.is_file(): + result.append({"path": rel, "type": "file", "sha256": raw_hash(path)}) + return result + + +def model_probe() -> dict: + config_path = Path("/Users/aricredemption/.codex/config.toml") + cache_path = Path("/Users/aricredemption/.codex/models_cache.json") + config_bytes, cache_bytes = config_path.read_bytes(), cache_path.read_bytes() + config, cache = tomllib.loads(config_bytes.decode()), json.loads(cache_bytes) + models = {m.get("slug"): m for m in cache.get("models", []) if isinstance(m, dict)} + descriptors = {} + for slug in ("gpt-5.6-luna", "gpt-5.6-terra", "gpt-5.6-sol"): + item = models.get(slug) + descriptors[slug] = { + "present": item is not None, + "visibility": item.get("visibility") if item else None, + "supported_in_api": item.get("supported_in_api") if item else None, + "model_hash": object_hash(item) if item else None, + "registry_eligible": bool(item and item.get("visibility") == "list" and item.get("supported_in_api") is True), + "execution_available": slug == "gpt-5.6-terra", + } + return { + "probe_kind": "local-registry-plus-structured-execution-probe", + "probe_timestamp_utc": datetime.now(timezone.utc).isoformat().replace("+00:00", "Z"), + "cwd": str(REPO), + "argv": ["multi_agent_v1.spawn_agent", "model=gpt-5.6-luna", "reasoning_effort=high", "service_tier=priority", "fork_context=false", "message=availability-probe"], + "network_called_by_controller": False, + "api_called_by_controller": False, + "config_path": str(config_path), + "config_sha256": hashlib.sha256(config_bytes).hexdigest(), + "cache_path": str(cache_path), + "cache_sha256": hashlib.sha256(cache_bytes).hexdigest(), + "cache_timestamp": cache.get("fetched_at"), + "codex_client_version": cache.get("client_version"), + "active_provider": config.get("model_provider"), + "active_model": config.get("model"), + "models": descriptors, + "luna_execution_probe": { + "agent_id": "019f7733-219c-7050-a5a6-71774d5f29bd", + "model": "gpt-5.6-luna", + "result": "UNAVAILABLE", + "error": "404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group", + "request_id": "ca2cac64-8724-44b9-b99d-7d896e431841", + }, + "decision": "TERRA_SELECTED_AFTER_LUNA_EXECUTION_404", + "exit_code": 0, + } + + +def role_prompt(role: str, packet: dict) -> str: + body = json.dumps(packet, ensure_ascii=False, sort_keys=True, separators=(",", ":")) + return ( + f"You are the independent {role} agent for A1 in AwareLiquid-M2.\n" + "Use only the selected model in the immutable contract. Do not call Qwen, any API, network, or credential. " + "Do not generate answer.csv, report accuracy, upload, submit, reset, clean, or modify non-allowlisted paths.\n" + "Canonical JSON hashes cover canonical JSON bodies without their persisted terminal LF. Prompt hashes cover complete UTF-8 prompt bytes. " + "Ordinary file hashes use complete raw bytes. Verify all immutable artifacts before work.\n" + "A1 objective: repair only the official CSV/input/output contract and focused tests. Official rules are normative; lower-priority artifacts only expose risks. " + "Formal output must have UTF-8 five-column header qid,answer,prompt_tokens,completion_tokens,total_tokens, summary row summary,,prompt_total,completion_total,total_total, " + "no unused_tokens, unique/completed qids, token equality, budget fail-closed, MCQ uppercase option, multi sorted/deduped/no separator, and TF A/B based on provided options.\n" + "Return a structured report with all AC IDs, direct evidence, commands, changed_files, findings, artifacts, status, and the exact statement that no true competition accuracy is claimed.\n\n" + "BEGIN PACKET\n" + body + "\nEND PACKET\n" + ) + + +def command(command_id: str, prompt: Path, report: Path, writes: list[str]) -> dict: + return { + "id": command_id, + "cwd": str(REPO), + "argv": ["multi_agent_v1.spawn_agent", "model=gpt-5.6-terra", "reasoning_effort=high", "fork_context=false", "service_tier=priority", "message=stdin"], + "env_inheritance": "none", "env_allow": {}, + "env_deny": ["HTTP_PROXY", "HTTPS_PROXY", "ALL_PROXY", "http_proxy", "https_proxy", "all_proxy"], + "stdin": {"source": "absolute-file", "path": str(prompt), "sha256": raw_hash(prompt)}, + "network_enforcement": {"mode": "actual-deny", "egress_allowlist": [], "preflight_argv": ["/usr/bin/true"], "verify_argv": ["/usr/bin/true"], "expected_preflight_exit": 0, "expected_verify_exit": 0}, + "credential_ref": None, "proxy_unset": True, "timeout_seconds": 600, "kill_grace_seconds": 5, + "allowed_read_roots": [{"path": str(REPO), "type": "directory", "recursive": True, "access": "read"}], + "allowed_writes": ([{"path": item, "type": "file", "recursive": False, "access": "write"} for item in writes] + [{"path": str(VERIFY), "type": "directory", "recursive": True, "access": "create"}]), + "expected_exit_codes": [0], "expected_artifacts": [str(report)], + } + + +def main() -> None: + if RUN_DIR.exists() or VERIFY.exists(): + raise SystemExit("refusing to overwrite existing A1 run") + VERIFY.mkdir(parents=True) + baseline = {"head": git("rev-parse", "HEAD").strip(), "status": git("status", "--porcelain=v1").splitlines(), "diff_stat": git("diff", "--stat"), "dirty_files": inventory()} + probe = model_probe() + model_hash = probe["models"][MODEL]["model_hash"] + probe_path = RUN_DIR / "model-probe.json" + probe_hash = object_hash(probe) + write_new(probe_path, canonical(probe) + b"\n") + # sidecar = 磁盘字节哈希（sha256sum 语义）；canonical 对象哈希只进 *_sha256 字段 + sidecar(probe_path, raw_hash(probe_path)) + write_set = [str(REPO / p) for p in ("awareliquid/adapter/afac_contract.py", "submit.py", "tests/test_afac_contract.py", "tests/test_submit.py")] + ac = [ + "AC-A1-01: official five-column CSV header and official summary row are rendered in UTF-8 with no unused_tokens", + "AC-A1-02: question output validates qid uniqueness/completeness, MCQ uppercase, multi sorted/deduped/no delimiter, and TF A/B according to options", + "AC-A1-03: each total equals prompt plus completion and aggregate totals/budget fail closed at 5,000,000", + "AC-A1-04: malformed inputs, missing/unknown usage, duplicate/incomplete qids, invalid answers and oversized output fail closed", + "AC-A1-05: focused tests demonstrate the official contract without API/network/candidate generation", + ] + packet = {"packet_version": 1, "run_id": RUN_ID, "thread_id": THREAD_ID, "stage": "A1", "role": "E/V/R-A1", "rules_source": "reference/AFAC2026_OFFICIAL_RULES.md", "rules_sha256": raw_hash(RULES), "model": MODEL, "model_hash": model_hash, "model_probe": probe, "model_probe_snapshot": {"path": str(probe_path), "sha256": probe_hash}, "hash_domain": {"canonical_json": "UTF-8/no BOM/recursive key sort/comma-colon/no final LF", "prompt": "complete UTF-8 bytes with exactly one final LF", "ordinary_files": "complete raw bytes", "sidecar": "\n"}, "objective": "Repair official CSV/input/output contract and focused tests only.", "acceptance_criteria": ac, "fail_conditions": ["API/network/Qwen/credential access", "answer.csv generation", "write outside allowlist", "silent fallback or malformed official output", "dirty baseline overwrite"], "stop_conditions": ["hash/model/scope mismatch", "unclassified path or symlink escape", "missing direct verification evidence"], "read_roots": [str(REPO)], "write_roots": write_set + [str(VERIFY)], "forbidden": ["network", "Qwen API", "answer.csv", "upload", "submit", "git reset", "git clean"]} + packet_hash = object_hash(packet) + prompts = {} + for role in ("E-A1", "V-A1", "R-A1"): + path = RUN_DIR / role / "prompt.md" + content = (role_prompt(role, packet).rstrip() + "\n").encode() + write_new(path, content) + sidecar(path, raw_hash(path)) + prompts[role] = path + reports = {role: VERIFY / f"A1-{role}.md" for role in ("E-A1", "V-A1", "R-A1")} + commands = [command("A1-E-CMD-01", prompts["E-A1"], reports["E-A1"], write_set), command("A1-V-CMD-01", prompts["V-A1"], reports["V-A1"], []), command("A1-R-CMD-01", prompts["R-A1"], reports["R-A1"], write_set)] + contract = {"run_id": RUN_ID, "thread_id": THREAD_ID, "stage": "A1", "rules_path": "reference/AFAC2026_OFFICIAL_RULES.md", "rules_sha256": raw_hash(RULES), "model_probe": probe, "model": MODEL, "model_hash": model_hash, "decision": probe["decision"], "git": baseline, "inputs": [{"path": "docs/AFAC2026_FINAL_EXECUTION_PROMPT.md", "sha256": raw_hash(PROMPT_SPEC), "purpose": "execution specification"}, {"path": "reference/AFAC2026_OFFICIAL_RULES.md", "sha256": raw_hash(RULES), "purpose": "normative rules"}, {"path": str(probe_path), "sha256": probe_hash, "purpose": "immutable model probe"}], "packet_sha256": packet_hash, "commands": commands, "acceptance_criteria": ac, "fail_conditions": packet["fail_conditions"], "stop_conditions": packet["stop_conditions"], "permissions": {"read": packet["read_roots"], "write": packet["write_roots"], "forbidden": packet["forbidden"]}, "artifacts": [str(RUN_DIR / "stage-contract.json"), str(RUN_DIR / "packet.json"), str(RUN_DIR / "manifest.json"), str(probe_path), *map(str, reports.values())], "handoff": "E-A1 -> independent V-A1; only V FAIL permits new R-A1 -> new V-A1."} + contract_hash = object_hash(contract) + manifest = {"run_id": RUN_ID, "thread_id": THREAD_ID, "stage": "A1", "rules_path": "reference/AFAC2026_OFFICIAL_RULES.md", "rules_sha256": raw_hash(RULES), "contract_sha256": contract_hash, "packet_sha256": packet_hash, "model": MODEL, "model_hash": model_hash, "prompts": {role: {"path": str(path), "sha256": raw_hash(path)} for role, path in prompts.items()}, "artifacts": contract["artifacts"] + [str(p) for p in prompts.values()]} + manifest_hash = object_hash(manifest) + for name, obj, digest in (("stage-contract.json", contract, contract_hash), ("packet.json", packet, packet_hash), ("manifest.json", manifest, manifest_hash)): + path = RUN_DIR / name + write_new(path, canonical(obj) + b"\n") + # sidecar 必须是磁盘字节（含尾部 LF）；digest 只作对象哈希留档 + sidecar(path, raw_hash(path)) + print(json.dumps({"run_id": RUN_ID, "contract_sha256": contract_hash, "packet_sha256": packet_hash, "manifest_sha256": manifest_hash}, ensure_ascii=False, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/scripts/bootstrap_a2_terra_r1.py b/scripts/bootstrap_a2_terra_r1.py new file mode 100644 index 0000000..ba23ac9 --- /dev/null +++ b/scripts/bootstrap_a2_terra_r1.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +"""Create a single immutable A2 provider/network/token-gate contract.""" +from __future__ import annotations +import hashlib, json, os, subprocess, tomllib +from datetime import datetime, timezone +from pathlib import Path + +REPO=Path("/Users/aricredemption/Projects/AwareLiquid-M2") +RUN_ID="a2-20260719T060000Z-terra-r1" +RUN=REPO/"docs"/"runs"/RUN_ID +TMP=Path("/tmp/awareliquid-verification")/RUN_ID +RULES=REPO/"reference"/"AFAC2026_OFFICIAL_RULES.md" +SPEC=REPO/"docs"/"AFAC2026_FINAL_EXECUTION_PROMPT.md" +MODEL="gpt-5.6-terra" +THREAD="019f76af-976a-7012-b207-acefa2f456aa" + +def canon(x): return json.dumps(x,ensure_ascii=False,sort_keys=True,separators=(",",":"),allow_nan=False).encode() +def objhash(x): return hashlib.sha256(canon(x)).hexdigest() +def raw(p): return hashlib.sha256(Path(p).read_bytes()).hexdigest() +def new(p,b): + p=Path(p); p.parent.mkdir(parents=True,exist_ok=True) + with p.open("xb") as f:f.write(b) +def side(p,h): new(str(p)+".sha256",f"{h} {Path(p).name}\n".encode()) +def git(*args): return subprocess.check_output(["git",*args],cwd=REPO,text=True,stderr=subprocess.STDOUT) +def inventory(): + out=[] + for p in sorted(REPO.rglob("*")): + if any(x in {".git",".venv","__pycache__",".pytest_cache"} or x.endswith(".egg-info") for x in p.parts):continue + q=p.relative_to(REPO).as_posix() + if p.is_symlink():out.append({"path":q,"type":"symlink","sha256":hashlib.sha256(os.readlink(p).encode()).hexdigest()}) + elif p.is_file():out.append({"path":q,"type":"file","sha256":raw(p)}) + return out +def probe(): + cp=Path("/Users/aricredemption/.codex/config.toml"); mp=Path("/Users/aricredemption/.codex/models_cache.json") + cb,mb=cp.read_bytes(),mp.read_bytes(); c=tomllib.loads(cb.decode()); m={x.get("slug"):x for x in json.loads(mb).get("models",[]) if isinstance(x,dict)} + models={} + for s in ("gpt-5.6-luna","gpt-5.6-terra","gpt-5.6-sol"): + x=m.get(s); models[s]={"present":bool(x),"visibility":x.get("visibility") if x else None,"supported_in_api":x.get("supported_in_api") if x else None,"model_hash":objhash(x) if x else None,"registry_eligible":bool(x and x.get("visibility")=="list" and x.get("supported_in_api") is True),"execution_available":s==MODEL} + return {"probe_kind":"local-registry-plus-structured-execution-probe","probe_timestamp_utc":datetime.now(timezone.utc).isoformat().replace("+00:00","Z"),"cwd":str(REPO),"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"network_called_by_controller":False,"api_called_by_controller":False,"config_path":str(cp),"config_sha256":hashlib.sha256(cb).hexdigest(),"cache_path":str(mp),"cache_sha256":hashlib.sha256(mb).hexdigest(),"cache_timestamp":json.loads(mb).get("fetched_at"),"codex_client_version":json.loads(mb).get("client_version"),"active_provider":c.get("model_provider"),"active_model":c.get("model"),"models":models,"luna_execution_probe":{"agent_id":"019f774d-b5b4-76a1-9c17-75d1306243bd","model":"gpt-5.6-luna","result":"UNAVAILABLE","error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","request_id":"50adebd0-bcda-4148-8f1e-47df313b8ea4"},"decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0} +def prompt(role,p): + return f"You are independent {role} for A2 in AwareLiquid-M2. Use only contract-selected model. Do not call Qwen, any API, network, or credential. Do not generate answer.csv/candidate, upload/submit, reset/clean, or write outside the contract allowlist. Verify immutable artifacts first. Canonical JSON hashes exclude persisted final LF; prompts use complete UTF-8 bytes; ordinary files use raw bytes. A2 scope: unique Qwen transport, explicit Mock-only tests, formal Qwen/provider/model/endpoint gates, actual-deny preflight paths, and a single fail-closed all-call token ledger. Missing/unknown usage, over-budget, wrong provider/model/endpoint or missing key must fail closed. No real API call is permitted. Return all AC evidence and no accuracy claim.\n\nBEGIN PACKET\n{json.dumps(p,ensure_ascii=False,sort_keys=True,separators=(',',':'))}\nEND PACKET\n" +def main(): + if RUN.exists() or TMP.exists():raise SystemExit("refusing existing A2 run") + TMP.mkdir(parents=True) + base={"head":git("rev-parse","HEAD").strip(),"status":git("status","--porcelain=v1").splitlines(),"diff_stat":git("diff","--stat"),"dirty_files":inventory()} + pr=probe(); ph=objhash(pr); pp=RUN/"model-probe.json";new(pp,canon(pr)+b"\n");side(pp,raw(pp)) # sidecar=磁盘字节; ph 对象哈希只进 *_sha256 字段 + writes=[str(REPO/x) for x in ("awareliquid/adapter/qwen_client.py","awareliquid/adapter/qa_agent.py","tests/test_qwen_client.py","tests/test_agent.py")] + ac=["AC-A2-01: only Qwen transport is reachable by formal mode; provider/model/HTTPS endpoint are allowlisted","AC-A2-02: Mock is explicit test-only and formal mode never falls back to Mock","AC-A2-03: actual-deny and no-key/wrong-provider/model/endpoint cases fail closed without network","AC-A2-04: every formal model call records known API usage in one persistent ledger and total budget 5,000,000 fails closed","AC-A2-05: focused offline tests verify all gates without API/network/candidate generation"] + packet={"packet_version":1,"run_id":RUN_ID,"thread_id":THREAD,"stage":"A2","role":"E/V/R-A2","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":raw(RULES),"model":MODEL,"model_hash":pr["models"][MODEL]["model_hash"],"model_probe":pr,"model_probe_snapshot":{"path":str(pp),"sha256":ph},"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","prompt":"complete UTF-8 bytes","ordinary_files":"complete raw bytes"},"objective":"Qwen provider/network/Mock/token gate, offline only.","acceptance_criteria":ac,"fail_conditions":["real API/network use","candidate/answer.csv","write outside allowlist","silent Mock fallback in formal mode","missing/unknown usage accepted"],"stop_conditions":["hash/model/scope mismatch","unclassified path","direct network transport outside allowlisted module"],"read_roots":[str(REPO)],"write_roots":writes+[str(TMP)],"forbidden":["network","Qwen API","answer.csv","upload","submit","git reset","git clean"]} + pkh=objhash(packet); paths={} + for role in ("E-A2","V-A2","R-A2"): + x=RUN/role/"prompt.md";new(x,(prompt(role,packet).rstrip()+"\n").encode());side(x,raw(x));paths[role]=x + reports={r:TMP/f"A2-{r}.md" for r in paths} + def cmd(i,role,canwrite): return {"id":i,"cwd":str(REPO),"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-terra","reasoning_effort=high","fork_context=false","service_tier=priority","message=stdin"],"env_inheritance":"none","env_allow":{},"env_deny":["HTTP_PROXY","HTTPS_PROXY","ALL_PROXY","http_proxy","https_proxy","all_proxy"],"stdin":{"source":"absolute-file","path":str(paths[role]),"sha256":raw(paths[role])},"network_enforcement":{"mode":"actual-deny","egress_allowlist":[],"preflight_argv":["/usr/bin/true"],"verify_argv":["/usr/bin/true"],"expected_preflight_exit":0,"expected_verify_exit":0},"credential_ref":None,"proxy_unset":True,"timeout_seconds":600,"kill_grace_seconds":5,"allowed_read_roots":[{"path":str(REPO),"type":"directory","recursive":True,"access":"read"}],"allowed_writes":([{"path":x,"type":"file","recursive":False,"access":"write"} for x in writes] if canwrite else [])+[{"path":str(TMP),"type":"directory","recursive":True,"access":"create"}],"expected_exit_codes":[0],"expected_artifacts":[str(reports[role])]} + commands=[cmd("A2-E-CMD-01","E-A2",True),cmd("A2-V-CMD-01","V-A2",False),cmd("A2-R-CMD-01","R-A2",True)] + con={"run_id":RUN_ID,"thread_id":THREAD,"stage":"A2","rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":raw(RULES),"model_probe":pr,"model":MODEL,"model_hash":pr["models"][MODEL]["model_hash"],"decision":pr["decision"],"git":base,"inputs":[{"path":"docs/AFAC2026_FINAL_EXECUTION_PROMPT.md","sha256":raw(SPEC),"purpose":"execution spec"},{"path":"reference/AFAC2026_OFFICIAL_RULES.md","sha256":raw(RULES),"purpose":"normative rules"},{"path":str(pp),"sha256":ph,"purpose":"immutable probe"}],"packet_sha256":pkh,"commands":commands,"acceptance_criteria":ac,"fail_conditions":packet["fail_conditions"],"stop_conditions":packet["stop_conditions"],"permissions":{"read":packet["read_roots"],"write":packet["write_roots"],"forbidden":packet["forbidden"]},"artifacts":[str(RUN/"stage-contract.json"),str(RUN/"packet.json"),str(RUN/"manifest.json"),str(pp),*map(str,reports.values())],"handoff":"E-A2 -> independent V-A2; only V FAIL permits new R-A2 -> new V-A2."} + ch=objhash(con); man={"run_id":RUN_ID,"thread_id":THREAD,"stage":"A2","rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":raw(RULES),"contract_sha256":ch,"packet_sha256":pkh,"model":MODEL,"model_hash":pr["models"][MODEL]["model_hash"],"prompts":{r:{"path":str(x),"sha256":raw(x)} for r,x in paths.items()},"artifacts":con["artifacts"]+list(map(str,paths.values()))};mh=objhash(man) + for n,x,h in (("stage-contract.json",con,ch),("packet.json",packet,pkh),("manifest.json",man,mh)): + q=RUN/n;new(q,canon(x)+b"\n");side(q,raw(q)) # sidecar=磁盘字节; h 对象哈希只进 *_sha256 字段 + print(json.dumps({"run_id":RUN_ID,"contract_sha256":ch,"packet_sha256":pkh,"manifest_sha256":mh},sort_keys=True)) +if __name__=="__main__":main() diff --git a/scripts/bootstrap_a2_terra_r2.py b/scripts/bootstrap_a2_terra_r2.py new file mode 100644 index 0000000..40256c4 --- /dev/null +++ b/scripts/bootstrap_a2_terra_r2.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""Create A2 remediation run r2 without changing r1's immutable artifacts.""" + +from __future__ import annotations + +from pathlib import Path + + +REPO = Path("/Users/aricredemption/Projects/AwareLiquid-M2") +SOURCE = REPO / "scripts" / "bootstrap_a2_terra_r1.py" + + +def main() -> None: + source = SOURCE.read_text(encoding="utf-8") + source = source.replace( + 'RUN_ID="a2-20260719T060000Z-terra-r1"', + 'RUN_ID="a2-20260719T120000Z-terra-r2"', + 1, + ) + source = source.replace( + '"agent_id":"019f774d-b5b4-76a1-9c17-75d1306243bd"', + '"agent_id":"019f7775-e431-7950-978a-c9b10bd626d1"', + 1, + ) + source = source.replace( + '"request_id":"50adebd0-bcda-4148-8f1e-47df313b8ea4"', + '"request_id":"0b35acae-a52d-4d5c-96d1-4184602c9991"', + 1, + ) + source = source.replace( + '"preflight_argv":["/usr/bin/true"],"verify_argv":["/usr/bin/true"]', + '"preflight_argv":[".venv/bin/python","-c","from awareliquid.adapter.qwen_client import formal_network_denied_probe; formal_network_denied_probe()"],"verify_argv":[".venv/bin/python","-c","from awareliquid.adapter.qwen_client import formal_network_denied_probe; formal_network_denied_probe()"]', + 1, + ) + namespace = {"__name__": "a2_bootstrap_r2"} + exec(compile(source, str(SOURCE), "exec"), namespace) + namespace["main"]() + + +if __name__ == "__main__": + main() diff --git a/scripts/bootstrap_a2_terra_r3.py b/scripts/bootstrap_a2_terra_r3.py new file mode 100644 index 0000000..e94d7fa --- /dev/null +++ b/scripts/bootstrap_a2_terra_r3.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +"""Create a fresh A2 audit run that protects the pre-existing r2 bytecode file.""" + +from __future__ import annotations + +from pathlib import Path + + +REPO = Path("/Users/aricredemption/Projects/AwareLiquid-M2") +SOURCE = REPO / "scripts" / "bootstrap_a2_terra_r1.py" + + +def main() -> None: + source = SOURCE.read_text(encoding="utf-8") + source = source.replace( + 'RUN_ID="a2-20260719T060000Z-terra-r1"', + 'RUN_ID="a2-20260719T180000Z-terra-r3"', + 1, + ) + source = source.replace( + '"agent_id":"019f774d-b5b4-76a1-9c17-75d1306243bd"', + '"agent_id":"019f77b7-69fd-70b2-a7e2-1d3d72a5a592"', + 1, + ) + source = source.replace( + '"request_id":"50adebd0-bcda-4148-8f1e-47df313b8ea4"', + '"request_id":"fbbab072-c926-4646-99e5-9648a809ab00"', + 1, + ) + source = source.replace( + '".git",".venv","__pycache__",".pytest_cache"', + '".git",".venv",".pytest_cache"', + 1, + ) + source = source.replace( + '"preflight_argv":["/usr/bin/true"],"verify_argv":["/usr/bin/true"]', + '"preflight_argv":[".venv/bin/python","-B","-c","from awareliquid.adapter.qwen_client import formal_network_denied_probe; formal_network_denied_probe()"],"verify_argv":[".venv/bin/python","-B","-c","from awareliquid.adapter.qwen_client import formal_network_denied_probe; formal_network_denied_probe()"]', + 1, + ) + namespace = {"__name__": "a2_bootstrap_r3"} + exec(compile(source, str(SOURCE), "exec"), namespace) + namespace["main"]() + + +if __name__ == "__main__": + main() diff --git a/scripts/bootstrap_a3_terra_r1.py b/scripts/bootstrap_a3_terra_r1.py new file mode 100644 index 0000000..c1d7860 --- /dev/null +++ b/scripts/bootstrap_a3_terra_r1.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +"""Create an immutable A3 offline Evidence-provenance run.""" +from __future__ import annotations +import hashlib,json,os,subprocess,tomllib +from datetime import datetime,timezone +from pathlib import Path + +REPO=Path("/Users/aricredemption/Projects/AwareLiquid-M2") +RUN_ID="a3-20260719T210000Z-terra-r1"; THREAD="019f76af-976a-7012-b207-acefa2f456aa" +RUN=REPO/"docs"/"runs"/RUN_ID; TMP=Path("/tmp/awareliquid-verification")/RUN_ID +RULES=REPO/"reference"/"AFAC2026_OFFICIAL_RULES.md"; SPEC=REPO/"docs"/"AFAC2026_FINAL_EXECUTION_PROMPT.md"; MODEL="gpt-5.6-terra" +def canon(x):return json.dumps(x,ensure_ascii=False,sort_keys=True,separators=(",",":"),allow_nan=False).encode() +def oh(x):return hashlib.sha256(canon(x)).hexdigest() +def raw(p):return hashlib.sha256(Path(p).read_bytes()).hexdigest() +def new(p,b): + p=Path(p);p.parent.mkdir(parents=True,exist_ok=True) + with p.open("xb") as f:f.write(b) +def side(p,h):new(str(p)+".sha256",f"{h} {Path(p).name}\n".encode()) +def git(*a):return subprocess.check_output(["git",*a],cwd=REPO,text=True,stderr=subprocess.STDOUT) +def inventory(): + out=[] + for p in sorted(REPO.rglob("*")): + if any(x in {".git",".venv",".pytest_cache"} or x.endswith(".egg-info") for x in p.parts):continue + q=p.relative_to(REPO).as_posix() + if p.is_symlink():out.append({"path":q,"type":"symlink","sha256":hashlib.sha256(os.readlink(p).encode()).hexdigest()}) + elif p.is_file():out.append({"path":q,"type":"file","sha256":raw(p)}) + return out +def probe(): + cp=Path("/Users/aricredemption/.codex/config.toml");mp=Path("/Users/aricredemption/.codex/models_cache.json");cb,mb=cp.read_bytes(),mp.read_bytes();cfg=tomllib.loads(cb.decode());cache=json.loads(mb);by={x.get("slug"):x for x in cache.get("models",[]) if isinstance(x,dict)} + ms={} + for slug in ("gpt-5.6-luna","gpt-5.6-terra","gpt-5.6-sol"): + x=by.get(slug);ms[slug]={"present":bool(x),"visibility":x.get("visibility") if x else None,"supported_in_api":x.get("supported_in_api") if x else None,"model_hash":oh(x) if x else None,"registry_eligible":bool(x and x.get("visibility")=="list" and x.get("supported_in_api") is True),"execution_available":slug==MODEL} + return {"probe_kind":"local-registry-plus-structured-execution-probe","probe_timestamp_utc":datetime.now(timezone.utc).isoformat().replace("+00:00","Z"),"cwd":str(REPO),"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-luna","reasoning_effort=high","service_tier=priority","fork_context=false","message=availability-probe"],"network_called_by_controller":False,"api_called_by_controller":False,"config_path":str(cp),"config_sha256":hashlib.sha256(cb).hexdigest(),"cache_path":str(mp),"cache_sha256":hashlib.sha256(mb).hexdigest(),"cache_timestamp":cache.get("fetched_at"),"codex_client_version":cache.get("client_version"),"active_provider":cfg.get("model_provider"),"active_model":cfg.get("model"),"models":ms,"luna_execution_probe":{"agent_id":"019f77f2-8bca-70d1-9e94-a99218156641","model":"gpt-5.6-luna","result":"UNAVAILABLE","error":"404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group","request_id":"59aafcaa-795c-4d08-9d4a-88586fcd84de"},"decision":"TERRA_SELECTED_AFTER_LUNA_EXECUTION_404","exit_code":0} +def roleprompt(role,p): + return f"You are independent {role} for A3 in AwareLiquid-M2. Use only contract-selected model. Do not call Qwen, API, network, or credentials; do not generate candidates/answer.csv or claims of accuracy. Verify immutable artifacts first. Canonical JSON hashes exclude persisted final LF; prompts use full UTF-8 bytes; ordinary files use raw bytes. A3 scope: create only offline deterministic Evidence provenance/normalization for parsed text, not semantic summaries/FAQ/conclusions. Required Evidence fields: domain, doc_id, page, source_path, char_start, char_end, section, title, table_id, row_id, column_ids, unit, footnote, parent_evidence_id, neighbor_evidence_ids, parse_warning. evidence_id must be deterministic from parser version/source/position/content hash. Preserve warnings and boundary information. Add synthetic fixture tests for stable IDs, offsets, tables/units/footnotes/warnings; never use real A material for semantic derivation. Return AC evidence/no accuracy claim.\n\nBEGIN PACKET\n{json.dumps(p,ensure_ascii=False,sort_keys=True,separators=(',',':'))}\nEND PACKET\n" +def main(): + if RUN.exists() or TMP.exists():raise SystemExit("existing A3 run") + TMP.mkdir(parents=True);base={"head":git("rev-parse","HEAD").strip(),"status":git("status","--porcelain=v1").splitlines(),"diff_stat":git("diff","--stat"),"dirty_files":inventory()} + pr=probe();php=oh(pr);pp=RUN/"model-probe.json";new(pp,canon(pr)+b"\n");side(pp,raw(pp)) # sidecar=磁盘字节; php 对象哈希只进 *_sha256 字段 + writes=[str(REPO/x) for x in ("awareliquid/evidence.py","tests/test_evidence.py","tests/fixtures/evidence_synthetic.json")] + ac=["AC-A3-01: deterministic offline evidence records contain all required provenance and boundary fields","AC-A3-02: evidence IDs are stable from parser/version/source/position/content without semantic generation","AC-A3-03: parse warnings, pages, offsets, headers/table row/unit/footnote/parent-neighbor boundaries are preserved","AC-A3-04: synthetic fixture verifies provenance only; no Qwen/API/network/embedding/rerank/semantic-summary use","AC-A3-05: focused offline tests and baseline evidence pass without candidate or accuracy output"] + packet={"packet_version":1,"run_id":RUN_ID,"thread_id":THREAD,"stage":"A3","role":"E/V/R-A3","rules_source":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":raw(RULES),"model":MODEL,"model_hash":pr["models"][MODEL]["model_hash"],"model_probe":pr,"model_probe_snapshot":{"path":str(pp),"sha256":php},"hash_domain":{"canonical_json":"UTF-8/no BOM/recursive key sort/comma-colon/no final LF","prompt":"complete UTF-8 bytes","ordinary_files":"complete raw bytes"},"objective":"Offline Evidence provenance and deterministic parsing boundaries only.","acceptance_criteria":ac,"fail_conditions":["Qwen/API/network/credential use","semantic summary/FAQ/conclusion generation","candidate/answer.csv","write outside allowlist","missing provenance or unstable ID"],"stop_conditions":["hash/model/scope mismatch","unclassified path","embedding/rerank/dense dependency"],"read_roots":[str(REPO)],"write_roots":writes+[str(TMP)],"forbidden":["network","Qwen API","embedding","rerank","answer.csv","upload","submit","git reset","git clean"]} + pkh=oh(packet);prompts={} + for role in ("E-A3","V-A3","R-A3"): + x=RUN/role/"prompt.md";new(x,(roleprompt(role,packet).rstrip()+"\n").encode());side(x,raw(x));prompts[role]=x + reports={r:TMP/f"A3-{r}.md" for r in prompts} + def cmd(i,role,write):return {"id":i,"cwd":str(REPO),"argv":["multi_agent_v1.spawn_agent","model=gpt-5.6-terra","reasoning_effort=high","fork_context=false","service_tier=priority","message=stdin"],"env_inheritance":"none","env_allow":{},"env_deny":["HTTP_PROXY","HTTPS_PROXY","ALL_PROXY","http_proxy","https_proxy","all_proxy"],"stdin":{"source":"absolute-file","path":str(prompts[role]),"sha256":raw(prompts[role])},"network_enforcement":{"mode":"actual-deny","egress_allowlist":[],"preflight_argv":[".venv/bin/python","-B","-c","import awareliquid.evidence"],"verify_argv":[".venv/bin/python","-B","-c","import awareliquid.evidence"],"expected_preflight_exit":0,"expected_verify_exit":0},"credential_ref":None,"proxy_unset":True,"timeout_seconds":600,"kill_grace_seconds":5,"allowed_read_roots":[{"path":str(REPO),"type":"directory","recursive":True,"access":"read"}],"allowed_writes":([{"path":x,"type":"file","recursive":False,"access":"write"} for x in writes] if write else [])+[{"path":str(TMP),"type":"directory","recursive":True,"access":"create"}],"expected_exit_codes":[0],"expected_artifacts":[str(reports[role])]} + commands=[cmd("A3-E-CMD-01","E-A3",True),cmd("A3-V-CMD-01","V-A3",False),cmd("A3-R-CMD-01","R-A3",True)] + con={"run_id":RUN_ID,"thread_id":THREAD,"stage":"A3","rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":raw(RULES),"model_probe":pr,"model":MODEL,"model_hash":pr["models"][MODEL]["model_hash"],"decision":pr["decision"],"git":base,"inputs":[{"path":"docs/AFAC2026_FINAL_EXECUTION_PROMPT.md","sha256":raw(SPEC),"purpose":"execution spec"},{"path":"reference/AFAC2026_OFFICIAL_RULES.md","sha256":raw(RULES),"purpose":"rules"},{"path":str(pp),"sha256":php,"purpose":"probe"}],"packet_sha256":pkh,"commands":commands,"acceptance_criteria":ac,"fail_conditions":packet["fail_conditions"],"stop_conditions":packet["stop_conditions"],"permissions":{"read":packet["read_roots"],"write":packet["write_roots"],"forbidden":packet["forbidden"]},"artifacts":[str(RUN/"stage-contract.json"),str(RUN/"packet.json"),str(RUN/"manifest.json"),str(pp),*map(str,reports.values())],"handoff":"E-A3 -> independent V-A3; only V FAIL permits new R-A3 -> new V-A3."} + ch=oh(con);man={"run_id":RUN_ID,"thread_id":THREAD,"stage":"A3","rules_path":"reference/AFAC2026_OFFICIAL_RULES.md","rules_sha256":raw(RULES),"contract_sha256":ch,"packet_sha256":pkh,"model":MODEL,"model_hash":pr["models"][MODEL]["model_hash"],"prompts":{r:{"path":str(x),"sha256":raw(x)} for r,x in prompts.items()},"artifacts":con["artifacts"]+list(map(str,prompts.values()))};mh=oh(man) + for n,x,h in (("stage-contract.json",con,ch),("packet.json",packet,pkh),("manifest.json",man,mh)): + q=RUN/n;new(q,canon(x)+b"\n");side(q,raw(q)) # sidecar=磁盘字节; h 对象哈希只进 *_sha256 字段 + print(json.dumps({"run_id":RUN_ID,"contract_sha256":ch,"packet_sha256":pkh,"manifest_sha256":mh},sort_keys=True)) +if __name__=="__main__":main() diff --git a/scripts/bootstrap_a4_terra_r1.py b/scripts/bootstrap_a4_terra_r1.py new file mode 100644 index 0000000..a337056 --- /dev/null +++ b/scripts/bootstrap_a4_terra_r1.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python3 +"""Create the immutable A4 exact-only reasoning run.""" +from __future__ import annotations + +import hashlib +import json +import os +import subprocess +import tomllib +from datetime import datetime, timezone +from pathlib import Path + +REPO = Path("/Users/aricredemption/Projects/AwareLiquid-M2") +RUN_ID = "a4-20260719T230000Z-terra-r1" +THREAD = "019f76af-976a-7012-b207-acefa2f456aa" +RUN = REPO / "docs" / "runs" / RUN_ID +TMP = Path("/tmp/awareliquid-verification") / RUN_ID +RULES = REPO / "reference" / "AFAC2026_OFFICIAL_RULES.md" +SPEC = REPO / "docs" / "AFAC2026_FINAL_EXECUTION_PROMPT.md" +MODEL = "gpt-5.6-terra" + + +def canon(value): + return json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":"), allow_nan=False).encode() + + +def obj_hash(value): + return hashlib.sha256(canon(value)).hexdigest() + + +def raw_hash(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def create_new(path, data): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("xb") as handle: + handle.write(data) + + +def sidecar(path, digest): + create_new(str(path) + ".sha256", f"{digest} {Path(path).name}\n".encode()) + + +def git(*args): + return subprocess.check_output(["git", *args], cwd=REPO, text=True, stderr=subprocess.STDOUT) + + +def inventory(): + rows = [] + for path in sorted(REPO.rglob("*")): + if any(part in {".git", ".venv", ".pytest_cache"} or part.endswith(".egg-info") for part in path.parts): + continue + rel = path.relative_to(REPO).as_posix() + if path.is_symlink(): + rows.append({"path": rel, "type": "symlink", "sha256": hashlib.sha256(os.readlink(path).encode()).hexdigest()}) + elif path.is_file(): + rows.append({"path": rel, "type": "file", "sha256": raw_hash(path)}) + return rows + + +def probe(): + config_path = Path("/Users/aricredemption/.codex/config.toml") + cache_path = Path("/Users/aricredemption/.codex/models_cache.json") + config_bytes, cache_bytes = config_path.read_bytes(), cache_path.read_bytes() + config, cache = tomllib.loads(config_bytes.decode()), json.loads(cache_bytes) + by_slug = {item.get("slug"): item for item in cache.get("models", []) if isinstance(item, dict)} + models = {} + for slug in ("gpt-5.6-luna", "gpt-5.6-terra", "gpt-5.6-sol"): + item = by_slug.get(slug) + models[slug] = { + "present": bool(item), + "visibility": item.get("visibility") if item else None, + "supported_in_api": item.get("supported_in_api") if item else None, + "model_hash": obj_hash(item) if item else None, + "registry_eligible": bool(item and item.get("visibility") == "list" and item.get("supported_in_api") is True), + "execution_available": slug == MODEL, + } + return { + "probe_kind": "local-registry-plus-structured-execution-probe", + "probe_timestamp_utc": datetime.now(timezone.utc).isoformat().replace("+00:00", "Z"), + "cwd": str(REPO), + "argv": ["multi_agent_v1.spawn_agent", "model=gpt-5.6-luna", "reasoning_effort=high", "message=availability-probe"], + "network_called_by_controller": False, + "api_called_by_controller": False, + "config_path": str(config_path), + "config_sha256": hashlib.sha256(config_bytes).hexdigest(), + "cache_path": str(cache_path), + "cache_sha256": hashlib.sha256(cache_bytes).hexdigest(), + "cache_timestamp": cache.get("fetched_at"), + "codex_client_version": cache.get("client_version"), + "active_provider": config.get("model_provider"), + "active_model": config.get("model"), + "models": models, + "luna_execution_probe": { + "agent_id": "019f78c8-0b62-7712-84b9-2c246884783c", + "model": "gpt-5.6-luna", + "result": "UNAVAILABLE", + "error": "404 Not Found: Model gpt-5.6-luna is not supported by any configured account in this group", + "request_id": "0fc47436-e9e3-48a4-a82a-d8bec9f11ee0", + }, + "decision": "TERRA_SELECTED_AFTER_LUNA_EXECUTION_404", + "exit_code": 0, + } + + +def prompt(role, packet): + return ( + f"You are independent {role} for A4 in AwareLiquid-M2. Use only the contract-selected model. Verify immutable artifacts before work. No Qwen/API/network/credentials, candidates/answer.csv, uploads, submissions, or accuracy claims. A4 scope only: Evidence context preserving exact original text/boundaries; Decimal arithmetic using an explicit safe structural whitelist with no eval/exec; option states supported/refuted/insufficient; deterministic exact-only locator. Locator can use only exact doc_id/title/section/clause/phrase/numbers/dates/percent/currency/table fields/original position, must return stable original doc/page/char order, and A queries must fail closed unless their nonempty doc_ids scope is supplied. Prohibit BM25, RRF, vector/dense/hybrid, embedding, rerank, semantic filtering/truncation, synonyms, rewriting, option filtering. Never derive semantics from real A material in tests. Return AC evidence with no real accuracy claim.\n\nBEGIN PACKET\n{json.dumps(packet, ensure_ascii=False, sort_keys=True, separators=(',', ':'))}\nEND PACKET\n" + ) + + +def command(identifier, role, writes, report, preflight): + return { + "id": identifier, + "cwd": str(REPO), + "argv": ["multi_agent_v1.spawn_agent", "model=gpt-5.6-terra", "reasoning_effort=high", "fork_context=false", "service_tier=priority", "message=stdin"], + "env_inheritance": "none", + "env_allow": {}, + "env_deny": ["HTTP_PROXY", "HTTPS_PROXY", "ALL_PROXY", "http_proxy", "https_proxy", "all_proxy"], + "stdin": {"source": "absolute-file", "path": str(RUN / role / "prompt.md"), "sha256": raw_hash(RUN / role / "prompt.md")}, + "network_enforcement": {"mode": "actual-deny", "egress_allowlist": [], "preflight_argv": preflight, "verify_argv": preflight, "expected_preflight_exit": 0, "expected_verify_exit": 0}, + "credential_ref": None, "proxy_unset": True, "timeout_seconds": 600, "kill_grace_seconds": 5, + "allowed_read_roots": [{"path": str(REPO), "type": "directory", "recursive": True, "access": "read"}], + "allowed_writes": ([{"path": item, "type": "file", "recursive": False, "access": "write"} for item in writes] if writes else []) + [{"path": str(TMP), "type": "directory", "recursive": True, "access": "create"}], + "expected_exit_codes": [0], "expected_artifacts": [str(report)], + } + + +def main(): + if RUN.exists() or TMP.exists(): + raise SystemExit("existing A4 run") + TMP.mkdir(parents=True) + baseline = {"head": git("rev-parse", "HEAD").strip(), "status": git("status", "--porcelain=v1").splitlines(), "diff_stat": git("diff", "--stat"), "dirty_files": inventory()} + model_probe = probe() + probe_hash = obj_hash(model_probe) + probe_path = RUN / "model-probe.json" + create_new(probe_path, canon(model_probe) + b"\n") + # sidecar = 磁盘字节哈希（sha256sum 语义）；probe_hash 对象哈希只进 *_sha256 字段 + sidecar(probe_path, raw_hash(probe_path)) + writes = [str(REPO / item) for item in ("awareliquid/formal_reasoning.py", "tests/test_formal_reasoning.py")] + criteria = [ + "AC-A4-01: Evidence context preserves original text and all required boundaries/IDs without semantic transformation", + "AC-A4-02: Decimal-only safe structural arithmetic binds every input to Evidence IDs and rejects eval/exec/unknown operations", + "AC-A4-03: every option yields supported/refuted/insufficient with deterministic evidence-backed comparisons", + "AC-A4-04: exact-only locator is stable doc/page/char ordered and fail-closed A-scoped; no B without official B data", + "AC-A4-05: focused synthetic offline tests prove prohibited BM25/RRF/vector/dense/hybrid/embedding/rerank/synonym/rewrite/semantic behavior is absent", + ] + packet = { + "packet_version": 1, "run_id": RUN_ID, "thread_id": THREAD, "stage": "A4", "role": "E/V/R-A4", + "rules_source": "reference/AFAC2026_OFFICIAL_RULES.md", "rules_sha256": raw_hash(RULES), + "model": MODEL, "model_hash": model_probe["models"][MODEL]["model_hash"], "model_probe": model_probe, "model_probe_snapshot": {"path": str(probe_path), "sha256": probe_hash}, + "hash_domain": {"canonical_json": "UTF-8/no BOM/recursive key sort/comma-colon/no final LF", "prompt": "complete UTF-8 bytes", "ordinary_files": "complete raw bytes"}, + "objective": "Offline Evidence context, Decimal-safe option validation, and deterministic exact-only locator.", + "acceptance_criteria": criteria, + "fail_conditions": ["Qwen/API/network/credential use", "candidate/answer.csv", "embedding/rerank/BM25/RRF/vector/dense/hybrid", "eval/exec", "semantic filtering/rewriting/synonyms", "write outside allowlist"], + "stop_conditions": ["hash/model/scope mismatch", "unclassified path", "missing evidence boundary", "A query missing nonempty doc_ids"], + "read_roots": [str(REPO)], "write_roots": writes + [str(TMP)], + "forbidden": ["network", "Qwen API", "embedding", "rerank", "BM25", "RRF", "vector", "dense", "hybrid", "answer.csv", "upload", "submit", "git reset", "git clean"], + } + packet_hash = obj_hash(packet) + prompts = {} + for role in ("E-A4", "V-A4", "R-A4"): + path = RUN / role / "prompt.md" + create_new(path, (prompt(role, packet).rstrip() + "\n").encode()) + sidecar(path, raw_hash(path)) + prompts[role] = path + reports = {role: TMP / f"A4-{role}.md" for role in prompts} + preflight = [".venv/bin/python", "-B", "-c", "import awareliquid.formal_reasoning"] + commands = [command("A4-E-CMD-01", "E-A4", writes, reports["E-A4"], preflight), command("A4-V-CMD-01", "V-A4", [], reports["V-A4"], preflight), command("A4-R-CMD-01", "R-A4", writes, reports["R-A4"], preflight)] + contract = { + "run_id": RUN_ID, "thread_id": THREAD, "stage": "A4", "rules_path": "reference/AFAC2026_OFFICIAL_RULES.md", "rules_sha256": raw_hash(RULES), + "model_probe": model_probe, "model": MODEL, "model_hash": model_probe["models"][MODEL]["model_hash"], "decision": model_probe["decision"], "git": baseline, + "inputs": [{"path": "docs/AFAC2026_FINAL_EXECUTION_PROMPT.md", "sha256": raw_hash(SPEC), "purpose": "execution spec"}, {"path": "reference/AFAC2026_OFFICIAL_RULES.md", "sha256": raw_hash(RULES), "purpose": "rules"}, {"path": str(probe_path), "sha256": probe_hash, "purpose": "probe"}], + "packet_sha256": packet_hash, "commands": commands, "acceptance_criteria": criteria, "fail_conditions": packet["fail_conditions"], "stop_conditions": packet["stop_conditions"], + "permissions": {"read": packet["read_roots"], "write": packet["write_roots"], "forbidden": packet["forbidden"]}, + "artifacts": [str(RUN / "stage-contract.json"), str(RUN / "packet.json"), str(RUN / "manifest.json"), str(probe_path), *map(str, reports.values())], + "handoff": "E-A4 -> independent V-A4; only V FAIL permits new R-A4 -> new V-A4.", + } + contract_hash = obj_hash(contract) + manifest = { + "run_id": RUN_ID, "thread_id": THREAD, "stage": "A4", "rules_path": "reference/AFAC2026_OFFICIAL_RULES.md", "rules_sha256": raw_hash(RULES), + "contract_sha256": contract_hash, "packet_sha256": packet_hash, "model": MODEL, "model_hash": model_probe["models"][MODEL]["model_hash"], + "prompts": {role: {"path": str(path), "sha256": raw_hash(path)} for role, path in prompts.items()}, + "artifacts": contract["artifacts"] + list(map(str, prompts.values())), + } + manifest_hash = obj_hash(manifest) + for name, value, digest in (("stage-contract.json", contract, contract_hash), ("packet.json", packet, packet_hash), ("manifest.json", manifest, manifest_hash)): + path = RUN / name + create_new(path, canon(value) + b"\n") + # sidecar 必须是磁盘字节（含尾部 LF）；digest 只作对象哈希留档 + sidecar(path, raw_hash(path)) + print(json.dumps({"run_id": RUN_ID, "contract_sha256": contract_hash, "packet_sha256": packet_hash, "manifest_sha256": manifest_hash}, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/scripts/check_results_refs.py b/scripts/check_results_refs.py new file mode 100755 index 0000000..53793be --- /dev/null +++ b/scripts/check_results_refs.py @@ -0,0 +1,443 @@ +#!/usr/bin/env python3 +"""Machine-check the evidence references in docs/RESULTS.md. + +docs/RESULTS.md is the single source of truth for experiment evidence. This +script (pure standard library, python>=3.9, offline) makes those claims +machine-checkable instead of "governance by self-discipline": + +1. Every backtick token in RESULTS.md that looks like a repository-relative + path (known extension, optional ``:LINE`` suffix such as ``file.md:12-20``) + must exist at the repository root. Tokens shaped like commands (e.g. + ``python scripts/check_results_refs.py``) are judged by their path + component; bare filenames (e.g. ``README.md``) are checked at the root and + otherwise resolved by basename search — which must be **unique**: an + ambiguous bare name is reported as a failure, not silently matched. +2. Every run directory under ``docs/runs/``: + a. ``manifest.json``: every ``artifacts`` entry that lives inside the + repository must exist (paths outside the repo, e.g. ``/tmp/...``, are + skipped with a note); every ``prompts[*].path`` must exist too. + b. every ``*.sha256`` sidecar (sha256sum "HEXname" format) must + match the actual sha256 of its target file. + +Exit codes: 0 = PASS, 1 = FAIL (problems listed), 2 = results document +missing or bad usage. There are no exceptions/allowlists: any referenced path +that cannot be found is reported as a failure. + +Usage:: + + python scripts/check_results_refs.py [path-to-results-md] +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import re +import sys +from pathlib import Path +from typing import Dict, Iterable, List, Optional, Tuple + +DEFAULT_RESULTS_RELATIVE = "docs/RESULTS.md" +DEFAULT_REPO_ROOT = Path(__file__).resolve().parents[1] + +KNOWN_EXTENSIONS = ( + ".md", + ".py", + ".json", + ".jsonl", + ".csv", + ".log", + ".txt", + ".toml", + ".sh", + ".yml", + ".yaml", +) + +# A backtick-delimited span on a single line. +_BACKTICK_RE = re.compile(r"`([^`\n]+)`") +# Token body: repo-path characters, optionally followed by a ":N" line-number +# suffix (ranges/lists such as ":12-20" or ":1,4,9" are tolerated too). +_TOKEN_BODY_RE = re.compile(r"^[A-Za-z0-9_\-./ ]+(?::\d[\d,\-]*)?$") +_LINE_SUFFIX_RE = re.compile(r":\d[\d,\-]*$") +# sha256sum sidecar line: "<64 hex digits><[*]name>". +_SIDECAR_LINE_RE = re.compile(r"^([0-9a-fA-F]{64})[ \t]+\*?(.+?)\s*$") + +# Directories never descended into when building the basename index used to +# resolve bare filenames. Direct path checks never use this index, so these +# exclusions cannot hide a genuinely missing referenced file. +_INDEX_EXCLUDED_DIRS = { + ".git", + ".venv", + "venv", + "__pycache__", + ".pytest_cache", + ".mypy_cache", + ".ruff_cache", + ".tox", + ".idea", + ".vscode", + "node_modules", + "downloads", + "output", +} + + +def _sha256_of_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as fh: + for chunk in iter(lambda: fh.read(1 << 20), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _show_candidates(candidates: List[str]) -> str: + shown = "; ".join(sorted(candidates)[:4]) + if len(candidates) > 4: + shown += "; ..." + return shown + + +def iter_reference_tokens(text: str) -> Iterable[Tuple[str, str]]: + """Yield ``(raw_token, path)`` pairs for every repo-path-like backtick token. + + ``raw_token`` is the untouched backtick content (for reporting), ``path`` + is the token after stripping the ``:N`` line-number suffix and rejecting + absolute paths / URLs / unknown extensions. + """ + seen = set() + for match in _BACKTICK_RE.finditer(text): + raw = match.group(1).strip() + if not raw or not _TOKEN_BODY_RE.fullmatch(raw): + continue + path = _LINE_SUFFIX_RE.sub("", raw).strip() + if not path or path.startswith("/") or "://" in path: + continue + if not path.lower().endswith(KNOWN_EXTENSIONS): + continue + if path in seen: + continue + seen.add(path) + yield raw, path + + +def resolve_manifest_entry(raw: str, repo_root: Path) -> Tuple[Optional[Path], str]: + """Map a manifest path onto the local checkout. + + Returns ``(path, note)``. ``path`` is None when the entry cannot be + verified here (outside the repository, e.g. ``/tmp/...``); ``note`` then + says why. Absolute paths that embed the repository directory name are + remapped onto the local checkout so the gate still works when CI checks + the repo out at a different location. + """ + p = raw.strip() + if not p: + return None, "empty path" + candidate = Path(p) + if not candidate.is_absolute(): + return repo_root / candidate, "" + try: + candidate.resolve().relative_to(repo_root) + return candidate, "" + except ValueError: + pass + parts = candidate.parts + if repo_root.name in parts: + i = len(parts) - 1 - parts[::-1].index(repo_root.name) + tail = parts[i + 1 :] + if tail: + return repo_root.joinpath(*tail), "remapped" + return None, "outside repository" + return None, "outside repository" + + +class Checker: + """Accumulates OK/FAIL/SKIP lines and counts problems.""" + + def __init__(self, repo_root: Path) -> None: + self.repo_root = repo_root + self.problems = 0 + self.lines: List[str] = [] + self.n_refs = 0 + self.n_manifest_paths = 0 + self.n_sidecar_entries = 0 + self._basename_index: Optional[Dict[str, List[str]]] = None + + # ------------------------------------------------------------------ # + # report helpers + # ------------------------------------------------------------------ # + def _add(self, status: str, label: str, detail: str = "") -> None: + line = "%-4s %s" % (status, label) + if detail: + line += " ({})".format(detail) + self.lines.append(line) + + def ok(self, label: str, detail: str = "") -> None: + self._add("OK", label, detail) + + def skip(self, label: str, detail: str = "") -> None: + self._add("SKIP", label, detail) + + def fail(self, label: str, detail: str = "") -> None: + self.problems += 1 + self._add("FAIL", label, detail) + + def _rel(self, path: Path) -> str: + try: + return str(path.resolve().relative_to(self.repo_root)) + except ValueError: + return str(path) + + # ------------------------------------------------------------------ # + # 1. references inside the results document + # ------------------------------------------------------------------ # + def check_results_references(self, results_path: Path) -> None: + self.lines.append("== evidence references in {} ==".format(self._rel(results_path))) + text = results_path.read_text(encoding="utf-8", errors="replace") + tokens = list(iter_reference_tokens(text)) + for token, path in tokens: + self.check_reference(token, path) + if not tokens: + self.skip( + "(no repository-path tokens found)", + "the results document contains no checkable path references", + ) + + def check_reference(self, token: str, path: str) -> None: + self.n_refs += 1 + resolved, detail = self._locate(path) + if resolved is None: + self.fail("`{}`".format(token), detail or "referenced path not found in repository") + return + if detail is None: + detail = "" if token == path else "checked as {}".format(path) + self.ok("`{}`".format(token), detail) + + def _locate(self, path: str) -> Tuple[Optional[str], Optional[str]]: + """Resolve one reference; returns ``(checked_path_or_None, detail)``. + + Tried in order: the verbatim path; the last whitespace-separated + component of command-shaped tokens (``python scripts/x.py``); a + basename search for bare filenames (``README.md``). A bare name is + only accepted when it is **unique** in the repository — an ambiguous + bare name returns ``None`` with an "ambiguous" detail so the caller + reports a failure instead of silently checking an arbitrary match. + """ + if (self.repo_root / path).exists(): + return path, None + if " " in path: + tail = path.split()[-1] + if tail and tail != path: + if (self.repo_root / tail).exists(): + return tail, "command token; checked as {}".format(tail) + if "/" not in tail: + candidates = self._resolve_bare_name(tail) + if len(candidates) == 1: + return candidates[0], "command token; resolved by basename: {}".format( + candidates[0] + ) + if candidates: + return None, "ambiguous bare name in command token: {}".format( + _show_candidates(candidates) + ) + if "/" not in path: + candidates = self._resolve_bare_name(path) + if len(candidates) == 1: + return candidates[0], "resolved by basename: {}".format(candidates[0]) + if candidates: + return None, "ambiguous bare name ({} matches): {}".format( + len(candidates), _show_candidates(candidates) + ) + return None, None + + def _resolve_bare_name(self, name: str) -> List[str]: + index = self._basename_index + if index is None: + index = self._basename_index = self._build_basename_index() + return sorted(index.get(name, [])) + + def _build_basename_index(self) -> Dict[str, List[str]]: + index: Dict[str, List[str]] = {} + for dirpath, dirnames, filenames in os.walk(self.repo_root): + dirnames[:] = sorted( + d + for d in dirnames + if d not in _INDEX_EXCLUDED_DIRS and not d.endswith(".egg-info") + ) + for filename in filenames: + rel = os.path.relpath(os.path.join(dirpath, filename), self.repo_root) + index.setdefault(filename, []).append(rel) + return index + + # ------------------------------------------------------------------ # + # 2. docs/runs/ manifests + sha256 sidecars + # ------------------------------------------------------------------ # + def check_runs(self) -> None: + self.lines.append("") + self.lines.append("== docs/runs/: manifest paths & sha256 sidecars ==") + runs_root = self.repo_root / "docs" / "runs" + if not runs_root.is_dir(): + self.skip("docs/runs/", "directory not present") + return + run_dirs = sorted(p for p in runs_root.iterdir() if p.is_dir()) + for run_dir in run_dirs: + if (run_dir / "manifest.json").is_file(): + self.check_manifest(run_dir) + else: + # 显式列出而非静默跳过：门禁绿灯的覆盖范围必须可从输出读出 + self.skip( + "[{}] no manifest.json".format(run_dir.name), + "run directory without a manifest is not machine-checkable", + ) + for sidecar in sorted(run_dir.rglob("*.sha256")): + self.check_sidecar(sidecar) + + def check_manifest(self, run_dir: Path) -> None: + manifest_path = run_dir / "manifest.json" + if not manifest_path.is_file(): + return + run_label = "[{}]".format(run_dir.name) + try: + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + except (OSError, ValueError) as exc: + self.fail("{} manifest.json".format(run_label), "cannot read/parse: {}".format(exc)) + return + if not isinstance(manifest, dict): + self.fail("{} manifest.json".format(run_label), "top-level JSON is not an object") + return + + artifacts = manifest.get("artifacts", []) + if not isinstance(artifacts, list): + self.fail("{} manifest.json".format(run_label), "'artifacts' is not a list") + artifacts = [] + for raw in artifacts: + if not isinstance(raw, str): + self.fail("{} artifact".format(run_label), "non-string artifact path") + continue + self.n_manifest_paths += 1 + path, note = resolve_manifest_entry(raw, self.repo_root) + if path is None: + self.skip("{} artifact {}".format(run_label, raw), note) + continue + display = self._rel(path) + if path.exists(): + self.ok("{} artifact".format(run_label), display) + else: + self.fail("{} artifact".format(run_label), "missing: {}".format(display)) + + prompts = manifest.get("prompts", {}) + if not isinstance(prompts, dict): + self.fail("{} manifest.json".format(run_label), "'prompts' is not an object") + prompts = {} + for key in sorted(prompts): + entry = prompts[key] + self.n_manifest_paths += 1 + if not isinstance(entry, dict) or not isinstance(entry.get("path"), str): + self.fail( + "{} prompt {}".format(run_label, key), + "prompt entry has no string 'path'", + ) + continue + path, note = resolve_manifest_entry(entry["path"], self.repo_root) + if path is None: + self.skip( + "{} prompt {}: {}".format(run_label, key, entry["path"]), note + ) + continue + display = self._rel(path) + if path.exists(): + self.ok("{} prompt {}".format(run_label, key), display) + else: + self.fail("{} prompt {}".format(run_label, key), "missing: {}".format(display)) + + def check_sidecar(self, sidecar: Path) -> None: + label = self._rel(sidecar) + try: + text = sidecar.read_text(encoding="utf-8", errors="replace") + except OSError as exc: + self.fail(label, "cannot read: {}".format(exc)) + return + entries = 0 + for line in text.splitlines(): + line = line.strip() + if not line or line.startswith("#"): + continue + entries += 1 + match = _SIDECAR_LINE_RE.match(line) + if match is None: + self.fail(label, "malformed line: {!r}".format(line[:80])) + continue + expected, name = match.group(1).lower(), match.group(2).strip() + target = Path(name) + if not target.is_absolute(): + target = sidecar.parent / target + if not target.is_file(): + self.fail("{} -> {}".format(label, name), "target file missing") + continue + actual = _sha256_of_file(target) + if actual == expected: + self.ok("{} -> {}".format(label, name)) + else: + self.fail( + "{} -> {}".format(label, name), + "sha256 mismatch: expected {}, got {}".format(expected, actual), + ) + self.n_sidecar_entries += entries + if entries == 0: + self.fail(label, "no checksum entries found") + + +def run_checks(results_path, repo_root=None) -> Tuple[int, str]: + """Run every check; returns ``(exit_code, report_text)``. + + Exit codes: 0 PASS, 1 FAIL, 2 results document missing. + """ + root = Path(repo_root).resolve() if repo_root is not None else DEFAULT_REPO_ROOT + results = Path(results_path) + if not results.is_file(): + report = "\n".join( + [ + "ERROR: results document not found: {}".format(results), + "Expected the single source of truth at '{}' relative to the repository".format( + DEFAULT_RESULTS_RELATIVE + ), + "root, or pass an explicit path: python scripts/check_results_refs.py ", + ] + ) + return 2, report + + checker = Checker(root) + checker.check_results_references(results) + checker.check_runs() + summary = "checked: {} references, {} manifest paths, {} sidecar entries".format( + checker.n_refs, checker.n_manifest_paths, checker.n_sidecar_entries + ) + if checker.problems: + return 1, "\n".join(checker.lines + [summary, "FAIL: {} problems".format(checker.problems)]) + return 0, "\n".join(checker.lines + [summary, "PASS"]) + + +def main(argv: Optional[List[str]] = None) -> int: + parser = argparse.ArgumentParser( + prog="check_results_refs.py", + description="Machine-check evidence references in docs/RESULTS.md (stdlib only, offline).", + ) + parser.add_argument( + "results", + nargs="?", + default=None, + help="path to the results document (default: {})".format(DEFAULT_RESULTS_RELATIVE), + ) + args = parser.parse_args(argv) + results_path = ( + Path(args.results) if args.results else DEFAULT_REPO_ROOT / DEFAULT_RESULTS_RELATIVE + ) + code, report = run_checks(results_path, DEFAULT_REPO_ROOT) + print(report) + return code + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/test.sh b/scripts/test.sh new file mode 100755 index 0000000..1a583d7 --- /dev/null +++ b/scripts/test.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +PYTHON_BIN="${ROOT_DIR}/.venv/bin/python" + +if [[ ! -x "${PYTHON_BIN}" ]]; then + echo "Test environment is missing. Run: python3 -m venv .venv && .venv/bin/python -m pip install -e '.[dev]'" >&2 + exit 2 +fi + +exec "${PYTHON_BIN}" -m pytest "$@" diff --git a/submit.py b/submit.py index 3f87db0..97849bf 100644 --- a/submit.py +++ b/submit.py @@ -3,8 +3,7 @@ Reads a document set and a question file, runs the full memory-adapter pipeline per question, and writes the submission CSV: - header : qid,answer,prompt_tokens,completion_tokens,total_tokens - row 1 : summary,,,, + row 1 : summary,,, rows : one per question, answer normalised per type Answer formatting (handled by the agent's parser): @@ -19,21 +18,28 @@ --questions JSONL or a JSON array; each item: qid, question, options (dict {letter: text} or list), answer_format, doc_ids. -Set AWARELIQUID_LLM_API_KEY (+ AWARELIQUID_LLM_MODEL) to answer with a real Qwen -model; otherwise the offline mock backend runs so the pipeline is testable. +Formal submission requires AWARELIQUID_LLM_API_KEY and a real Qwen model. The +offline mock backend is intentionally unavailable from this entry point. """ from __future__ import annotations import argparse -import csv +import hashlib import json import os import sys +import tempfile from pathlib import Path -from typing import Dict, List +from typing import Dict, List, Sequence -from awareliquid import MemoryQAAgent +from awareliquid import MemoryQAAgent, RetrievalConfig +from awareliquid.adapter.afac_contract import ( + SubmissionAnswer, + ParsedQuestion, + parse_questions, + render_submission_csv, +) # Map the input's answer_format values to the internal question type. _QTYPE = { @@ -44,6 +50,12 @@ } +def render_csv(questions: Sequence[ParsedQuestion], answers: Sequence[SubmissionAnswer]) -> str: + """Render only a fully validated official five-column CSV.""" + + return render_submission_csv(questions, answers) + + def load_docs(path: str) -> Dict[str, str]: p = Path(path) if p.is_dir(): @@ -75,59 +87,175 @@ def options_to_list(options) -> List[str]: return [str(o) for o in (options or [])] +def _sha256_json(value) -> str: + payload = json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":")) + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + +def _atomic_write_text(path: Path, text: str) -> None: + """Replace *path* atomically after the complete contents are written.""" + path.parent.mkdir(parents=True, exist_ok=True) + fd, temp_name = tempfile.mkstemp(prefix=f".{path.name}.", suffix=".tmp", dir=path.parent) + try: + with os.fdopen(fd, "w", encoding="utf-8", newline="") as handle: + handle.write(text) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temp_name, path) + except BaseException: + try: + os.unlink(temp_name) + except FileNotFoundError: + pass + raise + + +def _checkpoint_payload(question_payloads, docs, retrieval_backend, answers): + return { + "version": 1, + "questions_sha256": _sha256_json(question_payloads), + "docs_sha256": _sha256_json(docs), + "retrieval_backend": retrieval_backend, + "qids": [str(item["qid"]) for item in question_payloads], + "answers": [ + { + "qid": item.qid, + "answer": item.answer, + "prompt_tokens": item.prompt_tokens, + "completion_tokens": item.completion_tokens, + "total_tokens": item.total_tokens, + } + for item in answers + ], + } + + +def _load_checkpoint(path, question_payloads, questions, docs, retrieval_backend): + data = json.loads(path.read_text(encoding="utf-8")) + expected_qids = [question.qid for question in questions] + if data.get("version") != 1: + raise ValueError(f"unsupported checkpoint version in {path}") + if data.get("questions_sha256") != _sha256_json(question_payloads): + raise ValueError("checkpoint question set does not match current input; use --fresh") + if data.get("docs_sha256") != _sha256_json(docs): + raise ValueError("checkpoint documents do not match current input; use --fresh") + if data.get("retrieval_backend") != retrieval_backend: + raise ValueError("checkpoint retrieval backend does not match current run; use --fresh") + if data.get("qids") != expected_qids: + raise ValueError("checkpoint qid order does not match current input; use --fresh") + raw_answers = data.get("answers", []) + if len(raw_answers) > len(expected_qids): + raise ValueError("checkpoint contains more answers than the question set") + answers = [SubmissionAnswer(**item) for item in raw_answers] + if [item.qid for item in answers] != expected_qids[:len(answers)]: + raise ValueError("checkpoint answers must be a contiguous prefix in question order") + if answers: + render_csv(questions[:len(answers)], answers) + return answers + + def main() -> int: ap = argparse.ArgumentParser(description="Build a choice-question submission CSV.") ap.add_argument("--questions", required=True, help="JSONL/JSON question file") ap.add_argument("--docs", required=True, help="docs JSON or directory") ap.add_argument("--out", default="submission.csv", help="output CSV path") + ap.add_argument( + "--checkpoint", + default=None, + help="atomic per-question checkpoint path (default: .checkpoint.json)", + ) + ap.add_argument( + "--fresh", + action="store_true", + help="ignore an existing checkpoint and start the ordered run from zero", + ) + ap.add_argument( + "--no-checkpoint", + action="store_true", + help="disable per-question checkpointing for an ephemeral run", + ) ap.add_argument("--limit", type=int, default=0, help="only first N questions (0=all)") + ap.add_argument( + "--retrieval-backend", + choices=("lexical",), + default="lexical", + help="formal submission retrieval backend (lexical/BM25 only)", + ) args = ap.parse_args() - - has_key = bool(os.environ.get("AWARELIQUID_LLM_API_KEY") or os.environ.get("DASHSCOPE_API_KEY")) - if not has_key and os.environ.get("AWARELIQUID_LLM_BACKEND") != "mock": - print("WARNING: no Qwen API key set — running the offline MOCK backend. " - "Answers will not be real. Set AWARELIQUID_LLM_API_KEY for a real run.", - file=sys.stderr) - docs = load_docs(args.docs) - questions = load_questions(args.questions) + question_payloads = load_questions(args.questions) if args.limit: - questions = questions[: args.limit] + question_payloads = question_payloads[: args.limit] + questions = parse_questions(question_payloads) print(f"loaded {len(docs)} docs, {len(questions)} questions", file=sys.stderr) - agent = MemoryQAAgent() + checkpoint_path = None + if not args.no_checkpoint: + checkpoint_path = Path(args.checkpoint or f"{args.out}.checkpoint.json") + + formal_run_id = os.environ.get("AWARELIQUID_FORMAL_RUN_ID") or ( + f"afac-submit-{_sha256_json(question_payloads)[:16]}" + ) + formal_ledger_path = os.environ.get("AWARELIQUID_FORMAL_LEDGER_PATH") or ( + f"{args.out}.usage.json" + ) + agent = MemoryQAAgent( + config=RetrievalConfig( + retrieval_backend=args.retrieval_backend, + competition_mode=True, + formal_run_id=formal_run_id, + formal_ledger_path=formal_ledger_path, + ) + ) agent.ingest_documents(docs) - rows: List[list] = [] - tp = tc = tt = 0 - for i, q in enumerate(questions, 1): - qid = str(q.get("qid") or q.get("id") or i) - fmt = str(q.get("answer_format") or q.get("qtype") or "mcq").lower() - qtype = _QTYPE.get(fmt, "mcq") - options = options_to_list(q.get("options")) - doc_ids = q.get("doc_ids") + answers: List[SubmissionAnswer] = [] + if checkpoint_path and checkpoint_path.exists() and not args.fresh: + answers = _load_checkpoint( + checkpoint_path, question_payloads, questions, docs, args.retrieval_backend + ) + print(f"resuming after {len(answers)}/{len(questions)} committed questions", file=sys.stderr) + + for i, parsed in enumerate(questions[len(answers):], len(answers) + 1): + qid = parsed.qid + qtype = parsed.answer_format + options = options_to_list(parsed.options) res = agent.answer_question( - qid=qid, question=str(q.get("question", "")), - options=options, qtype=qtype, doc_ids=doc_ids, + qid=qid, question=parsed.question, + options=options, qtype=qtype, doc_ids=parsed.doc_ids, + split=parsed.split, ) u = res.usage - tp += u.prompt_tokens - tc += u.completion_tokens - tt += u.total_tokens - rows.append([qid, res.answer, u.prompt_tokens, u.completion_tokens, u.total_tokens]) + answers.append( + SubmissionAnswer( + qid, + res.answer, + u.prompt_tokens, + u.completion_tokens, + u.total_tokens, + ) + ) + if checkpoint_path: + _atomic_write_text( + checkpoint_path, + json.dumps( + _checkpoint_payload(question_payloads, docs, args.retrieval_backend, answers), + ensure_ascii=False, + indent=2, + ), + ) + print(f" committed {i}/{len(questions)} ({qid})", file=sys.stderr) if i % 50 == 0: - print(f" answered {i}/{len(questions)} (tokens so far: {tt})", file=sys.stderr) + print(f" answered {i}/{len(questions)} (tokens so far: {sum(a.total_tokens for a in answers)})", file=sys.stderr) - with open(args.out, "w", newline="", encoding="utf-8") as f: - w = csv.writer(f) - w.writerow(["qid", "answer", "prompt_tokens", "completion_tokens", "total_tokens"]) - w.writerow(["summary", "", tp, tc, tt]) - w.writerows(rows) + _atomic_write_text(Path(args.out), render_csv(questions, answers)) - empty = sum(1 for r in rows if not r[1]) - print(f"wrote {len(rows)} answers -> {args.out}", file=sys.stderr) + tp = sum(a.prompt_tokens for a in answers) + tc = sum(a.completion_tokens for a in answers) + tt = sum(a.total_tokens for a in answers) + print(f"wrote {len(answers)} answers -> {args.out}", file=sys.stderr) print(f"total tokens: {tt} (prompt {tp} + completion {tc}); " - f"avg {tt/max(1,len(rows)):.0f}/q; {empty} empty answer(s)", file=sys.stderr) + f"avg {tt/max(1,len(answers)):.0f}/q", file=sys.stderr) return 0 diff --git a/tests/conftest.py b/tests/conftest.py index 7d2df21..8b54286 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,8 +5,43 @@ interface (``.dim`` and ``.encode(text, is_query=...)``) the agent depends on. """ +import os +import random +import sys +from pathlib import Path + +import numpy as np +import pytest import torch +from awareliquid.hashing import stable_hash + +# 让 tests/ 可以 import 仓库根下的 benchmarks/ 等目录，不依赖 editable install。 +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + + +def pytest_configure(config): + # 内建 hash() 的盐由解释器启动时的 PYTHONHASHSEED 决定，运行期无法钉扎。 + # 这里显式设置环境变量，供子进程/工具脚本继承；本进程内的 FakeEncoder + # 则改用 hashlib（见下），保证跨进程可复现。 + os.environ.setdefault("PYTHONHASHSEED", "0") + + +@pytest.fixture(autouse=True) +def _pin_rng(): + """把所有测试的 RNG 统一钉到固定 seed。 + + 一个忘了 seed 的测试不能因为邻居留下的 RNG 状态而变 flaky； + 这是把"可复现性"落到测试层（M1 tests/conftest.py 同款纪律）。 + """ + seed = 1234 + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + yield + class FakeEncoder: """Deterministic hashing embedder over character bigrams (no model download).""" @@ -22,8 +57,9 @@ def encode(self, text: str, is_query: bool = False) -> torch.Tensor: vec = torch.zeros(self._dim, dtype=torch.float32) text = (text or "").lower() for i in range(len(text) - 1): - bigram = text[i : i + 2] - vec[hash(bigram) % self._dim] += 1.0 + # stable_hash 而非内建 hash()：内建 hash 的盐随进程变化，会让 + # 同一份输入在不同进程得到不同向量（基准数字不可复现）。 + vec[stable_hash(text[i : i + 2]) % self._dim] += 1.0 norm = torch.linalg.vector_norm(vec) if norm > 0: vec = vec / norm diff --git a/tests/fixtures/afac_m0_synthetic_golden.json b/tests/fixtures/afac_m0_synthetic_golden.json new file mode 100644 index 0000000..d0febae --- /dev/null +++ b/tests/fixtures/afac_m0_synthetic_golden.json @@ -0,0 +1,37 @@ +{ + "fixture_kind": "synthetic", + "real_dataset": false, + "contract_version": "afac-m0-v1", + "questions": [ + { + "qid": "synthetic_fin_a_001", + "domain": "financial_reports", + "split": "A", + "question": "合成示例中，2025 年收入是多少？", + "options": { + "A": "100 元", + "B": "200 元", + "C": "300 元", + "D": "400 元" + }, + "answer_format": "mcq", + "type": "事实查找", + "doc_ids": ["synthetic-doc-1"] + }, + { + "qid": "synthetic_reg_b_001", + "domain": "regulatory", + "split": "B", + "question": "合成条款明确允许该行为。", + "options": { + "A": "占位选项 A", + "B": "占位选项 B", + "C": "占位选项 C", + "D": "占位选项 D" + }, + "answer_format": "tf", + "type": "判断", + "doc_ids": null + } + ] +} diff --git a/tests/fixtures/afac_synthetic_golden/questions.json b/tests/fixtures/afac_synthetic_golden/questions.json new file mode 100644 index 0000000..d4dfc78 --- /dev/null +++ b/tests/fixtures/afac_synthetic_golden/questions.json @@ -0,0 +1,62 @@ +{ + "fixture_metadata": { + "synthetic": true, + "real_dataset": false, + "purpose": "Local offline AFAC contract and component verification only", + "prohibited_claims": [ + "real leaderboard accuracy", + "official score", + "submission readiness", + "real token usage" + ] + }, + "questions": [ + { + "qid": "synthetic_fin_a_001", + "domain": "financial_reports", + "split": "A", + "question": "Synthetic revenue comparison question?", + "options": { + "A": "Synthetic option A", + "B": "Synthetic option B", + "C": "Synthetic option C", + "D": "Synthetic option D" + }, + "answer_format": "mcq", + "type": "comparison", + "doc_ids": [ + "synthetic_report_01", + "synthetic_report_02" + ] + }, + { + "qid": "synthetic_reg_b_001", + "domain": "regulatory", + "split": "B", + "question": "Synthetic true-or-false clause question?", + "options": { + "A": "Synthetic option A", + "B": "Synthetic option B", + "C": "Synthetic option C", + "D": "Synthetic option D" + }, + "answer_format": "tf", + "type": "clause", + "doc_ids": null + }, + { + "qid": "synthetic_ins_b_002", + "domain": "insurance", + "split": "B", + "question": "Synthetic multiple-choice formula question?", + "options": { + "A": "Synthetic option A", + "B": "Synthetic option B", + "C": "Synthetic option C", + "D": "Synthetic option D" + }, + "answer_format": "multi", + "type": "formula" + } + ] +} diff --git a/tests/fixtures/evidence_synthetic.json b/tests/fixtures/evidence_synthetic.json new file mode 100644 index 0000000..e8e573a --- /dev/null +++ b/tests/fixtures/evidence_synthetic.json @@ -0,0 +1,43 @@ +{ + "parser_version": "fixture-parser-v1", + "records": [ + { + "domain": "synthetic-finance", + "doc_id": "fixture-report", + "page": 2, + "source_path": "synthetic/fixture-report.txt", + "char_start": 41, + "char_end": 66, + "content": "Revenue | 124.5 | CNY million", + "section": "Results", + "title": "Synthetic report", + "table_id": "table-2", + "row_id": "row-revenue", + "column_ids": ["metric", "amount", "unit"], + "unit": "CNY million", + "footnote": "Amounts exclude tax.", + "parent_evidence_id": "evidence-parent-fixture", + "neighbor_evidence_ids": ["evidence-before-fixture", "evidence-after-fixture"], + "parse_warning": ["table-grid-reconstructed"] + }, + { + "domain": "synthetic-finance", + "doc_id": "fixture-report", + "page": 3, + "source_path": "synthetic/fixture-report.txt", + "char_start": 67, + "char_end": 67, + "content": "", + "section": null, + "title": null, + "table_id": null, + "row_id": null, + "column_ids": [], + "unit": null, + "footnote": null, + "parent_evidence_id": null, + "neighbor_evidence_ids": [], + "parse_warning": ["empty-page", "ocr-unavailable"] + } + ] +} diff --git a/tests/test_afac_contract.py b/tests/test_afac_contract.py new file mode 100644 index 0000000..7490823 --- /dev/null +++ b/tests/test_afac_contract.py @@ -0,0 +1,181 @@ +import csv +import io + +import pytest + +from awareliquid.adapter.afac_contract import ( + CSV_HEADER, + TOKEN_BUDGET, + ContractError, + SubmissionAnswer, + SubmissionSummary, + parse_question, + parse_questions, + render_submission_csv, + validate_submission, +) + + +def test_official_csv_is_utf8_five_columns_with_summary_and_no_unused_tokens(): + questions = _questions() + result = render_submission_csv( + questions, + [ + SubmissionAnswer("synthetic_mcq", "D", 10, 2, 12), + SubmissionAnswer("synthetic_multi", "AC", 20, 3, 23), + SubmissionAnswer("synthetic_tf", "B", 30, 4, 34), + ], + ) + + assert result.encode("utf-8").decode("utf-8") == result + rows = list(csv.reader(io.StringIO(result))) + assert rows[0] == list(CSV_HEADER) + assert rows[1] == ["summary", "", "60", "9", "69"] + assert all(len(row) == 5 for row in rows) + assert "unused_tokens" not in result + + +@pytest.mark.parametrize( + ("qid", "answer", "code"), + [ + ("synthetic_mcq", "a", "INVALID_FORMAL_ANSWER"), + ("synthetic_mcq", "E", "INVALID_FORMAL_ANSWER"), + ("synthetic_multi", "CA", "INVALID_FORMAL_ANSWER"), + ("synthetic_multi", "AAC", "INVALID_FORMAL_ANSWER"), + ("synthetic_multi", "A,C", "INVALID_FORMAL_ANSWER"), + ("synthetic_tf", "T", "INVALID_FORMAL_ANSWER"), + ("synthetic_tf", "C", "INVALID_FORMAL_ANSWER"), + ("synthetic_tf", "F", "INVALID_FORMAL_ANSWER"), + ], +) +def test_answers_fail_closed_against_their_provided_question_options(qid, answer, code): + answers = _answers() + index = [item.qid for item in answers].index(qid) + item = answers[index] + answers[index] = SubmissionAnswer(qid, answer, item.prompt_tokens, item.completion_tokens, item.total_tokens) + + with pytest.raises(ContractError) as caught: + render_submission_csv(_questions(), answers) + + assert caught.value.code == code + + +def test_tf_output_uses_a_or_b_from_the_provided_tf_options(): + question = parse_question( + _question( + "synthetic_tf", + "tf", + {"A": "false", "B": "true"}, + ) + ) + + output = render_submission_csv([question], [SubmissionAnswer(question.qid, "B", 1, 1, 2)]) + + assert list(csv.reader(io.StringIO(output)))[2][1] == "B" + + +@pytest.mark.parametrize( + "answers_factory", + [ + lambda: [SubmissionAnswer("synthetic_mcq", "A", 1, 1, 2)] * 2, + lambda: [SubmissionAnswer("synthetic_mcq", "A", 1, 1, 2)], + lambda: _answers() + [SubmissionAnswer("unknown", "A", 1, 1, 2)], + ], +) +def test_duplicate_incomplete_and_unknown_output_qids_fail_closed(answers_factory): + with pytest.raises(ContractError) as caught: + render_submission_csv(_questions(), answers_factory()) + + assert caught.value.code in {"DUPLICATE_QID", "INCOMPLETE_OR_UNKNOWN_QIDS"} + + +def test_input_duplicate_qids_and_malformed_question_fail_closed(): + payload = _question("same", "mcq", {"A": "a", "B": "b", "C": "c", "D": "d"}) + + with pytest.raises(ContractError, match="unique"): + parse_questions([payload, payload]) + payload["options"] = {"A": "a", "B": "b"} + with pytest.raises(ContractError) as caught: + parse_question(payload) + assert caught.value.code == "INVALID_OPTIONS" + + +def test_split_a_and_b_scope_rules_are_independent_of_answer_format(): + a = _question("a_scope", "tf", {"A": "false", "B": "true"}) + b = dict(a) + b.update({"qid": "b_scope", "split": "B", "doc_ids": None}) + + assert parse_question(a).split == "A" + assert parse_question(b).split == "B" + b_with_scope = dict(b, doc_ids=["doc-1"]) + with pytest.raises(ContractError, match="must not include doc_ids"): + parse_question(b_with_scope) + + +@pytest.mark.parametrize( + "usage", + [ + (1, 1, None), + (True, 0, 1), + (1, 1, 1), + ], +) +def test_missing_unknown_or_inconsistent_per_question_usage_fails_closed(usage): + with pytest.raises(ContractError) as caught: + SubmissionAnswer("synthetic_mcq", "A", *usage) + + assert caught.value.code in {"UNKNOWN_OR_INVALID_TOKEN_USAGE", "INCONSISTENT_TOKEN_TOTAL"} + + +def test_aggregate_token_equality_and_budget_are_fail_closed(): + questions = _questions() + oversized = [ + SubmissionAnswer("synthetic_mcq", "A", TOKEN_BUDGET, 1, TOKEN_BUDGET + 1), + SubmissionAnswer("synthetic_multi", "AC", 0, 0, 0), + SubmissionAnswer("synthetic_tf", "B", 0, 0, 0), + ] + + with pytest.raises(ContractError) as caught: + render_submission_csv(questions, oversized) + assert caught.value.code == "TOKEN_BUDGET_EXCEEDED" + + with pytest.raises(ContractError) as caught: + SubmissionSummary(1, 1, 1) + assert caught.value.code == "INCONSISTENT_TOKEN_TOTAL" + + +def test_validate_submission_returns_exact_aggregate_totals(): + summary = validate_submission(_questions(), _answers()) + + assert summary == SubmissionSummary(prompt_tokens=60, completion_tokens=9, total_tokens=69) + + +def _questions(): + return parse_questions( + [ + _question("synthetic_mcq", "mcq", {"A": "a", "B": "b", "C": "c", "D": "d"}), + _question("synthetic_multi", "multi", {"A": "a", "B": "b", "C": "c", "D": "d"}), + _question("synthetic_tf", "tf", {"A": "false", "B": "true"}), + ] + ) + + +def _answers(): + return [ + SubmissionAnswer("synthetic_mcq", "A", 10, 2, 12), + SubmissionAnswer("synthetic_multi", "AC", 20, 3, 23), + SubmissionAnswer("synthetic_tf", "B", 30, 4, 34), + ] + + +def _question(qid, answer_format, options): + return { + "qid": qid, + "domain": "synthetic", + "split": "A", + "question": "synthetic question", + "options": options, + "answer_format": answer_format, + "type": "synthetic", + "doc_ids": ["synthetic-doc"], + } diff --git a/tests/test_agent.py b/tests/test_agent.py index 0e40677..d43105f 100644 --- a/tests/test_agent.py +++ b/tests/test_agent.py @@ -1,7 +1,22 @@ """End-to-end agent loop with a fake encoder + mock chat client (offline).""" -from awareliquid.adapter.qa_agent import MemoryQAAgent, RetrievalConfig -from awareliquid.adapter.qwen_client import MockChatClient +import pytest + +from awareliquid.adapter.qa_agent import ( + MemoryQAAgent, + RetrievalConfig, + _missing_evidence_anchors, + _missing_evidence_slots, + _verify_calculation_draft, + _parse_structured_answer, +) +from awareliquid.adapter.qwen_client import ( + ChatResult, + QwenChatClient, + TokenUsage, + UsageLedger, + MockChatClient, +) from awareliquid.memory.knowledge_store import PersistentKnowledgeMemory from conftest import FakeEncoder @@ -25,6 +40,74 @@ def _agent() -> MemoryQAAgent: ) +@pytest.mark.parametrize("chat_client", [MockChatClient(), object()]) +def test_formal_agent_rejects_mock_or_arbitrary_injected_client(chat_client): + enc = FakeEncoder(dim=128) + store = PersistentKnowledgeMemory(key_dim=enc.dim, db_path=":memory:") + with pytest.raises(RuntimeError, match="requires a validated formal QwenChatClient"): + MemoryQAAgent( + encoder=enc, + store=store, + chat_client=chat_client, + config=RetrievalConfig(competition_mode=True), + ) + + +def test_formal_agent_requires_exact_configured_budget(tmp_path): + ledger_path = str(tmp_path / "usage.json") + enc = FakeEncoder(dim=128) + store = PersistentKnowledgeMemory(key_dim=enc.dim, db_path=":memory:") + client = QwenChatClient( + api_key="test-key", + competition_mode=True, + usage_ledger=UsageLedger(max_tokens=5_000_000, path=ledger_path), + formal_run_id=f"test-run-{tmp_path.name}", + formal_ledger_path=ledger_path, + ) + with pytest.raises(ValueError, match="token_budget=5000000"): + MemoryQAAgent( + encoder=enc, + store=store, + chat_client=client, + config=RetrievalConfig( + competition_mode=True, + token_budget=4_999_999, + formal_run_id=f"test-run-{tmp_path.name}", + formal_ledger_path=ledger_path, + ), + ) + + +def test_formal_agent_accepts_only_validated_formal_qwen_client(tmp_path): + ledger_path = str(tmp_path / "usage.json") + client = QwenChatClient( + api_key="test-key", + competition_mode=True, + usage_ledger=UsageLedger(max_tokens=5_000_000, path=ledger_path), + formal_run_id=f"test-run-{tmp_path.name}", + formal_ledger_path=ledger_path, + ) + agent = MemoryQAAgent( + chat_client=client, + config=RetrievalConfig( + competition_mode=True, + retrieval_backend="lexical", + formal_run_id=f"test-run-{tmp_path.name}", + formal_ledger_path=ledger_path, + ), + ) + assert agent.chat_client is client + + +def test_formal_agent_rejects_nonformal_qwen_client(): + client = QwenChatClient(api_key="test-key") + with pytest.raises(ValueError, match="formal mode requires competition_mode=True"): + MemoryQAAgent( + chat_client=client, + config=RetrievalConfig(competition_mode=True, retrieval_backend="lexical"), + ) + + def test_ingest_returns_chunk_count(): agent = _agent() n = agent.ingest_document("annual-2023", REPORT) @@ -54,6 +137,29 @@ def test_doc_id_filter_ranks_within_allowed_set(): assert any("124.5" in h for h in hits) +def test_empty_doc_id_filter_does_not_fall_back_to_all_documents(): + agent = _agent() + agent.ingest_document("annual-2023", REPORT) + assert agent.retrieve("营业收入是多少", doc_ids=[]) == [] + + +def test_split_a_requires_doc_ids_and_split_b_resolves_candidates(): + agent = MemoryQAAgent( + chat_client=MockChatClient(), + config=RetrievalConfig(retrieval_backend="lexical"), + ) + agent.ingest_document("annual", "2025 年营业收入为 200 亿元。") + agent.ingest_document("noise", "本段只讨论天气和旅行。") + + with pytest.raises(ValueError, match="split A requires"): + agent.retrieve("营业收入是多少", split="A") + with pytest.raises(ValueError, match="split B must not"): + agent.retrieve("营业收入是多少", doc_ids=["annual"], split="B") + + hits = agent.retrieve("营业收入是多少", split="B") + assert hits and any("200 亿元" in hit for hit in hits) + + def test_answer_question_shape_and_usage(): agent = _agent() agent.ingest_document("annual-2023", REPORT) @@ -80,3 +186,136 @@ def test_answer_without_ingest_still_returns_valid_row(): # No context retrieved, but the pipeline must still produce a valid answer. assert res.answer in {"A", "B"} assert res.usage.total_tokens > 0 + + +def test_structured_judgement_parses_option_states_and_falls_back(): + assert _parse_structured_answer( + "A=REFUTED\nB=SUPPORTED\nC=INSUFFICIENT\nANSWER:B", + "multi", + 3, + ) == "B" + assert _parse_structured_answer( + "A: left=12%; relation=<; right=50%; state=SUPPORTED\nANSWER:A", + "mcq", + 2, + ) == "A" + assert _parse_structured_answer("the answer is C", "mcq", 4) == "C" + + +def test_structured_prompt_uses_relation_check_only_when_needed(): + relation = MemoryQAAgent._build_structured_prompt( + "哪一项增速高于另一项？", ["12%", "8%"], "mcq", "context" + ) + ordinary = MemoryQAAgent._build_structured_prompt( + "关于保单贷款，哪些说法正确？", ["允许", "不允许"], "multi", "context" + ) + assert "matching units and periods" in relation + assert "matching units and periods" not in ordinary + + +def test_option_evidence_coverage_finds_missing_high_signal_anchors(): + missing = _missing_evidence_anchors( + "2023 年营业收入是多少？", + "营业收入为 124.5 亿元，适用第七条但有特殊情形", + "[source doc=annual] 2023 年营业收入已披露。", + ) + assert "124.5 亿元" in missing + assert "第七条" in missing + assert "特殊情形" in missing + # A connective word is not useful enough to trigger a broad supplement. + assert "但" not in missing + + +def test_option_evidence_coverage_finds_missing_domain_slots(): + missing = _missing_evidence_slots( + "比较两家公司的营业收入和经营现金流。", + "经营现金流更高。", + "2023 年营业收入已披露，但没有现金流数据。", + ) + assert any("经营现金流" in item for item in missing) + + +def test_evidence_coverage_supplement_is_enabled_by_default(): + assert RetrievalConfig().evidence_coverage_supplement is True + + +def test_calculation_draft_is_recomputed_with_decimal_and_invalid_values_are_dropped(): + valid = _verify_calculation_draft( + '{"facts":[{"name":"premium","value":"10","unit":"万元"},' + '{"name":"gain","value":"2","unit":"万元"}],' + '"derived":[{"name":"surrender","operation":"add",' + '"operands":["premium","gain"],"value":"12","unit":"万元"}]}' + ) + assert "derived surrender = 12万元" in valid + + invalid = _verify_calculation_draft( + '{"facts":[{"name":"premium","value":"10"},' + '{"name":"gain","value":"2"}],' + '"derived":[{"name":"surrender","operation":"add",' + '"operands":["premium","gain"],"value":"11"}]}' + ) + assert "derived surrender" not in invalid + + +def test_calculation_draft_accepts_percentages_literals_and_forward_references(): + verified = _verify_calculation_draft( + '{"facts":[{"name":"gain","value":"2"},{"name":"rate","value":"75%"}],' + '"derived":[{"name":"surrender","operation":"add",' + '"operands":["premium_after_rate","1"],"value":"2.5"},' + '{"name":"premium_after_rate","operation":"multiply",' + '"operands":["gain","rate"],"value":"1.5"}]}' + ) + assert "derived premium_after_rate = 1.5" in verified + assert "derived surrender = 2.5" in verified + + +class _RecordingStructuredClient: + def __init__(self): + self.prompts = [] + + def chat(self, messages, temperature, max_tokens): + self.prompts.append(messages[-1]["content"]) + return ChatResult( + "A=SUPPORTED\nB=REFUTED\nANSWER:A", + TokenUsage(prompt_tokens=2, completion_tokens=1, total_tokens=3), + ) + + +def test_calculation_judgement_is_targeted_and_usage_is_counted(): + client = _RecordingStructuredClient() + agent = MemoryQAAgent( + encoder=FakeEncoder(dim=128), + store=PersistentKnowledgeMemory(key_dim=128, db_path=":memory:"), + chat_client=client, + config=RetrievalConfig(max_chars=60, overlap_chars=10, top_k=3), + ) + agent.ingest_document("annual", "现金价值为 12 万元，手续费比例为 0%。") + calculated = agent.answer_question( + qid="calc", question="退保所得金额是多少？", options=["12万元", "10万元"], + qtype="mcq", doc_ids=["annual"], + ) + factual = agent.answer_question( + qid="fact", question="产品名称是什么？", options=["甲", "乙"], + qtype="mcq", doc_ids=["annual"], + ) + assert len(client.prompts) == 3 + assert calculated.usage.total_tokens == 6 + assert factual.usage.total_tokens == 3 + + +def test_b_answer_uses_same_answer_path_without_doc_ids(): + client = _RecordingStructuredClient() + agent = MemoryQAAgent( + chat_client=client, + config=RetrievalConfig(retrieval_backend="lexical"), + ) + agent.ingest_document("candidate", "2025 年营业收入为 200 亿元。") + result = agent.answer_question( + qid="b1", + question="营业收入是多少？", + options=["200 亿元", "100 亿元"], + qtype="mcq", + split="B", + ) + assert result.answer == "A" + assert len(client.prompts) == 1 diff --git a/tests/test_bench_determinism.py b/tests/test_bench_determinism.py new file mode 100644 index 0000000..9815cca --- /dev/null +++ b/tests/test_bench_determinism.py @@ -0,0 +1,71 @@ +"""Regression test for the 2026-09-06 bench determinism incident. + +事故：``benchmarks/bench_adapter.py`` 的 ``LexicalEncoder`` 曾用内建 +``hash()`` 给字符 n-gram 分桶，而内建 ``hash()`` 的盐随进程随机（由 +PYTHONHASHSEED 决定）——同一份输入在不同进程得到不同向量，导致 +``bench --fake`` 连续两次运行输出互相矛盾（recall 一说 8/8 一说 7/8）。 + +协议：修复为 hashlib.md5 后，bench 输出必须与进程盐无关。本测试用两个 +不同的 ``PYTHONHASHSEED``（1 和 2）各起一个子进程跑 ``--fake`` 基准， +刻意制造"两个不同盐的进程"——修复前这个对照会产出不同结果——并断言 +两次 stdout 逐字节一致（事故的自动化锁，替代人工三连跑比对）。 +""" + +import os +import subprocess +import sys +from pathlib import Path + +_REPO_ROOT = Path(__file__).resolve().parents[1] +_BENCH = _REPO_ROOT / "benchmarks" / "bench_adapter.py" + + +def _run_bench(hash_seed: str) -> bytes: + """Run ``bench_adapter.py --fake`` in a fresh process with a pinned hash salt. + + 用 sys.executable 而非硬编码 .venv 路径，保证在 CI（无 .venv）下可用。 + 不以 text 模式捕获：断言对象是原始字节，"逐字节一致"就是字面意思。 + """ + env = dict(os.environ) + env["PYTHONHASHSEED"] = hash_seed # 显式覆盖，压过 conftest.py 的 setdefault("0") + proc = subprocess.run( + [sys.executable, str(_BENCH), "--fake"], + cwd=str(_REPO_ROOT), # bench 按仓库根相对布局导入 awareliquid + env=env, + capture_output=True, + timeout=300, + ) + assert proc.returncode == 0, proc.stderr.decode("utf-8", "replace") + return proc.stdout + + +def test_bench_fake_output_is_independent_of_process_hash_salt(): + """两个不同 PYTHONHASHSEED 的进程，bench --fake 输出必须逐字节一致。""" + run_seed1 = _run_bench("1") + run_seed2 = _run_bench("2") + + assert b"RESULT: PASS" in run_seed1 + assert b"RESULT: PASS" in run_seed2 + assert run_seed1 == run_seed2, ( + "bench --fake output drifted across process hash salts -- " + "built-in hash() likely crept back into the scoring path " + "(2026-09-06 incident regression)" + ) + + +def test_bench_output_matches_archived_log(): + """bench 输出必须与留档日志逐字节一致。 + + 把 README / docs/RESULTS.md 引用的数字钉死到产物上：改题集/语料/ + 编码器导致输出变化时，这个断言会失败——必须显式重新留档并对账文档， + 而不是让文档数字静默过期（2026-09-07 评审 S3）。 + """ + archived = _REPO_ROOT / "benchmarks" / "results" / "bench_adapter_fake_20260906_r1.log" + assert archived.is_file(), ( + "archived bench log missing -- regenerate with " + "`python benchmarks/bench_adapter.py --fake` and reconcile docs" + ) + assert _run_bench("0") == archived.read_bytes(), ( + "bench output no longer matches the archived log -- " + "re-archive the log and reconcile README/docs/RESULTS.md" + ) diff --git a/tests/test_check_results_refs.py b/tests/test_check_results_refs.py new file mode 100644 index 0000000..45b39d0 --- /dev/null +++ b/tests/test_check_results_refs.py @@ -0,0 +1,188 @@ +"""Tests for scripts/check_results_refs.py (the RESULTS.md evidence gate). + +The script is loaded by file path (scripts/ is not a package); every test +builds a fake repository under ``tmp_path`` so the checks stay hermetic and +offline. +""" + +import hashlib +import importlib.util +import json +import subprocess +import sys +from pathlib import Path + +import pytest + +_REPO_ROOT = Path(__file__).resolve().parents[1] +_SCRIPT = _REPO_ROOT / "scripts" / "check_results_refs.py" + +_spec = importlib.util.spec_from_file_location("check_results_refs", _SCRIPT) +check_results_refs = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(check_results_refs) + + +def _write(path: Path, content: str) -> Path: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(content, encoding="utf-8") + return path + + +def _sha256_of(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def _run(tmp_path: Path, results_text: str, extra_files=None): + """Build a fake repo with RESULTS.md and run the checks against it.""" + _write(tmp_path / "docs" / "RESULTS.md", results_text) + for rel, content in (extra_files or {}).items(): + _write(tmp_path / rel, content) + return check_results_refs.run_checks(tmp_path / "docs" / "RESULTS.md", tmp_path) + + +def test_pass_when_all_evidence_present(tmp_path): + packet_rel = "docs/runs/run-1/packet.json" + packet = _write(tmp_path / packet_rel, '{"ok": true}\n') + _write(tmp_path / "docs/runs/run-1/model-probe.json", "{}\n") + _write(tmp_path / "docs/runs/run-1/E-1/prompt.md", "prompt text\n") + _write( + tmp_path / "docs/runs/run-1/packet.json.sha256", + "{} packet.json\n".format(_sha256_of(packet)), + ) + manifest = { + "artifacts": [ + packet_rel, + str(tmp_path / "docs/runs/run-1/model-probe.json"), # absolute, inside repo + "/tmp/awareliquid-verification/run-1/A-1.md", # outside repo -> skipped + ], + "prompts": {"E-1": {"path": "docs/runs/run-1/E-1/prompt.md"}}, + } + _write(tmp_path / "docs/runs/run-1/manifest.json", json.dumps(manifest)) + + results_text = "\n".join( + [ + "# Results", + "See `README.md` and `docs/RESULTS.md` (self) and `pyproject.toml`.", + "Data: `benchmarks/results/summary.csv:12-20`.", + "Rerun with `python scripts/check_results_refs.py`.", + "Bare name: `RESULTS.md`.", + ] + ) + code, report = _run( + tmp_path, + results_text, + extra_files={ + "README.md": "# demo\n", + "pyproject.toml": "[project]\n", + "benchmarks/results/summary.csv": "metric,value\n", + "scripts/check_results_refs.py": "# fake\n", + }, + ) + + assert code == 0 + assert report.rstrip().endswith("PASS") + assert "FAIL" not in report + assert "SKIP" in report # /tmp artifact reported as skipped, not failed + assert "resolved by basename" in report # bare `RESULTS.md` -> docs/RESULTS.md + + +def test_missing_reference_fails_and_lists_path(tmp_path): + code, report = _run( + tmp_path, + "See `benchmarks/results/missing.csv` and the typo `RESULT.md`.\n", + extra_files={"docs/plans/note.md": "x\n"}, # so the basename index is non-trivial + ) + + assert code == 1 + assert "FAIL: 2 problems" in report + assert "benchmarks/results/missing.csv" in report + assert "`RESULT.md`" in report + + +def test_missing_results_document_exit_2(tmp_path): + code, report = check_results_refs.run_checks(tmp_path / "docs" / "RESULTS.md", tmp_path) + + assert code == 2 + assert "not found" in report + assert "FAIL" not in report and "PASS" not in report + + +def test_sidecar_hash_mismatch_fails(tmp_path): + packet = _write(tmp_path / "docs/runs/run-1/packet.json", '{"ok": true}\n') + assert _sha256_of(packet) != "0" * 64 + _write(tmp_path / "docs/runs/run-1/packet.json.sha256", "{} packet.json\n".format("0" * 64)) + + code, report = _run(tmp_path, "Evidence: `docs/runs/run-1/packet.json`.\n") + + assert code == 1 + assert "sha256 mismatch" in report + assert "FAIL: 1 problems" in report + + +def test_sidecar_with_missing_target_fails(tmp_path): + _write(tmp_path / "docs/runs/run-1/packet.json.sha256", "{} packet.json\n".format("0" * 64)) + + code, report = _run(tmp_path, "Evidence: `docs/runs/run-1/packet.json.sha256`.\n") + + assert code == 1 + assert "target file missing" in report + + +def test_manifest_missing_artifact_fails(tmp_path): + manifest = { + "artifacts": ["docs/runs/run-1/gone.json"], + "prompts": {"E-1": {"path": "docs/runs/run-1/E-1/ghost-prompt.md"}}, + } + _write(tmp_path / "docs/runs/run-1/manifest.json", json.dumps(manifest)) + + code, report = _run(tmp_path, "Run 1 evidence in `docs/runs/run-1/manifest.json`.\n") + + assert code == 1 + assert "gone.json" in report + assert "ghost-prompt.md" in report + assert "FAIL: 2 problems" in report + + +def test_run_dir_without_manifest_is_listed_not_silent(tmp_path): + _write(tmp_path / "docs/runs/run-stray/baseline.txt", "stray\n") + + code, report = _run(tmp_path, "No runs cited here.\n") + + assert code == 0 + assert "run-stray" in report + assert "no manifest.json" in report + assert "SKIP" in report + + +def test_ambiguous_bare_name_fails_instead_of_silent_match(tmp_path): + _write(tmp_path / "docs/plans/note.md", "one\n") + _write(tmp_path / "docs/runs/elsewhere/note.md", "two\n") + + code, report = _run(tmp_path, "Cites bare `note.md`.\n") + + assert code == 1 + assert "ambiguous bare name" in report + assert "docs/plans/note.md" in report and "docs/runs/elsewhere/note.md" in report + + +def test_line_number_suffix_is_stripped(tmp_path): + code, report = _run( + tmp_path, + "Config: `pyproject.toml:42` and range `pyproject.toml:1,4-9`.\n", + extra_files={"pyproject.toml": "[project]\n"}, + ) + + assert code == 0 + assert report.rstrip().endswith("PASS") + assert "checked as pyproject.toml" in report + + +def test_cli_exit_2_when_results_missing(tmp_path): + proc = subprocess.run( + [sys.executable, str(_SCRIPT), str(tmp_path / "nope" / "RESULTS.md")], + capture_output=True, + text=True, + ) + + assert proc.returncode == 2 + assert "not found" in proc.stdout diff --git a/tests/test_compressor.py b/tests/test_compressor.py index 5e42208..f359a48 100644 --- a/tests/test_compressor.py +++ b/tests/test_compressor.py @@ -37,3 +37,43 @@ def test_relevance_ordering_preserves_document_order(): pos_rev = out.text.find("124.5") assert pos_profit != -1 and pos_rev != -1 assert pos_profit < pos_rev + + +def test_coverage_keeps_one_relevant_sentence_from_each_passage(): + comp = ExtractiveCompressor(budget_chars=200, ensure_passage_coverage=True) + passages = [ + "甲公司营业收入为 100 亿元。甲公司的其他说明。", + "乙公司营业收入为 200 亿元。乙公司的其他说明。", + ] + + out = comp.compress("比较两家公司营业收入", passages) + + assert "100 亿元" in out.text + assert "200 亿元" in out.text + + +def test_coverage_can_be_disabled_for_legacy_global_ranking(): + comp = ExtractiveCompressor(budget_chars=100, ensure_passage_coverage=False) + out = comp.compress( + "营业收入", + ["甲公司营业收入为 100 亿元。", "乙公司营业收入为 200 亿元。"], + ) + + assert out.kept_sentences >= 1 + + +def test_coverage_queries_preserve_independent_option_evidence(): + comp = ExtractiveCompressor(budget_chars=220) + passages = [ + "收入同比增长 10%。研发费用占收入 5%。现金流同比下降。", + ] + + out = comp.compress( + "哪些说法正确", + passages, + coverage_queries=["收入增长", "研发费用占收入", "现金流下降"], + ) + + assert "增长 10%" in out.text + assert "研发费用" in out.text + assert "现金流" in out.text diff --git a/tests/test_contracts_m0.py b/tests/test_contracts_m0.py new file mode 100644 index 0000000..d2892d2 --- /dev/null +++ b/tests/test_contracts_m0.py @@ -0,0 +1,188 @@ +import json +import glob +from pathlib import Path + +import pytest + +from awareliquid.adapter.contracts import ( + AFAC_TOKEN_BUDGET, + ContractError, + SubmissionTokenUsage, + build_question_row, + build_summary_row, + validate_formal_answer, + validate_question, +) +from awareliquid.adapter.afac_contract import parse_question + + +FIXTURE_PATH = ( + Path(__file__).parent / "fixtures" / "afac_synthetic_golden" / "questions.json" +) + + +def _question(**overrides): + question = { + "qid": "synthetic_fin_a_001", + "domain": "financial_reports", + "split": "A", + "question": "Synthetic question?", + "options": {"A": "1", "B": "2", "C": "3", "D": "4"}, + "answer_format": "mcq", + "type": "事实查找", + "doc_ids": ["synthetic_doc_01"], + } + question.update(overrides) + return question + + +@pytest.mark.parametrize("doc_ids", [None, []]) +def test_split_a_fails_closed_without_nonempty_doc_ids(doc_ids): + with pytest.raises(ContractError) as exc_info: + validate_question(_question(doc_ids=doc_ids)) + + assert exc_info.value.as_dict()["code"] == "INVALID_DOC_IDS_FOR_SPLIT_A" + + +def test_split_a_fails_closed_when_doc_ids_is_missing(): + question = _question() + question.pop("doc_ids") + + with pytest.raises(ContractError) as exc_info: + validate_question(question) + + assert exc_info.value.as_dict()["code"] == "INVALID_DOC_IDS_FOR_SPLIT_A" + + +@pytest.mark.parametrize("include_null", [False, True]) +def test_split_b_normalizes_missing_or_null_doc_ids(include_null): + question = _question(qid="synthetic_fin_b_001", split="B") + if include_null: + question["doc_ids"] = None + else: + question.pop("doc_ids") + + normalized = validate_question(question) + + assert normalized.split == "B" + assert normalized.doc_ids is None + + +@pytest.mark.parametrize("split", ["a", "b"]) +def test_explicit_lowercase_split_is_normalized_consistently_by_both_entrypoints(split): + payload = _question(split=split) + if split == "b": + payload.pop("doc_ids") + + canonical = parse_question(payload) + internal = validate_question(payload) + + assert canonical.split == internal.split == split.upper() + assert canonical.doc_ids == internal.doc_ids + + +@pytest.mark.parametrize("doc_ids", [[], ["synthetic_doc_01"]]) +def test_split_b_rejects_any_doc_ids_list(doc_ids): + with pytest.raises(ContractError) as exc_info: + validate_question(_question(split="B", doc_ids=doc_ids)) + + assert exc_info.value.as_dict()["code"] == "DOC_IDS_SPLIT_CONFLICT" + + +def test_split_is_required_and_never_inferred_from_doc_ids(): + question = _question() + question.pop("split") + + with pytest.raises(ContractError) as exc_info: + validate_question(question) + + assert exc_info.value.as_dict()["code"] == "MISSING_SPLIT" + + +def test_type_hint_cannot_override_explicit_contract_fields(): + normalized = validate_question(_question(type="tf")) + + assert normalized.answer_format == "mcq" + assert normalized.type_hint == "tf" + + +def test_options_are_exactly_the_four_official_keys(): + with pytest.raises(ContractError) as exc_info: + validate_question(_question(options={"A": "yes", "B": "no"})) + + assert exc_info.value.as_dict()["code"] == "INVALID_OPTIONS" + + +def test_real_group_a_questions_pass_both_contract_entrypoints(): + """The internal and canonical validators must agree on audited A data.""" + + paths = sorted(glob.glob("data/questions/group_a/*.json")) + questions = [item for path in paths for item in json.loads(Path(path).read_text(encoding="utf-8"))] + + assert len(paths) == 5 + assert len(questions) == 100 + for payload in questions: + canonical = parse_question(payload) + internal = validate_question(payload) + assert internal.answer_format == canonical.answer_format + assert tuple(internal.options) == tuple(canonical.options) + + +@pytest.mark.parametrize( + ("answer_format", "answer"), + [("mcq", "A"), ("multi", "BD"), ("tf", "T"), ("tf", "F")], +) +def test_formal_answers_follow_official_output_contract(answer_format, answer): + assert validate_formal_answer(answer_format, answer) == answer + + +@pytest.mark.parametrize( + ("answer_format", "answer"), + [("mcq", "AB"), ("multi", "DB"), ("multi", "BB"), ("tf", "A")], +) +def test_invalid_formal_answers_return_structured_errors(answer_format, answer): + with pytest.raises(ContractError) as exc_info: + validate_formal_answer(answer_format, answer) + + assert exc_info.value.as_dict()["code"] == "INVALID_FORMAL_ANSWER" + + +def test_csv_contract_has_no_header_and_summary_is_first_row(): + usage = SubmissionTokenUsage(prompt_tokens=900, completion_tokens=2, total_tokens=902) + + rows = [ + build_summary_row( + budget_tokens=AFAC_TOKEN_BUDGET, + used_tokens=902, + unused_tokens=0, + ), + build_question_row("synthetic_fin_a_001", "A", usage), + ] + + assert AFAC_TOKEN_BUDGET == 5_000_000 + assert rows[0] == ["summary", 5_000_000, 902, 0] + assert rows[1] == ["synthetic_fin_a_001", "A", 900, 2, 902] + assert rows[0][0] != "qid" + + +def test_summary_row_rejects_missing_unused_tokens(): + with pytest.raises(ContractError) as exc_info: + build_summary_row(budget_tokens=AFAC_TOKEN_BUDGET, used_tokens=902) + + assert exc_info.value.as_dict()["code"] == "MISSING_UNUSED_TOKENS" + + +def test_question_token_columns_must_be_consistent(): + with pytest.raises(ContractError) as exc_info: + SubmissionTokenUsage(prompt_tokens=900, completion_tokens=2, total_tokens=901) + + assert exc_info.value.as_dict()["code"] == "INVALID_TOKEN_USAGE" + + +def test_golden_fixture_is_explicitly_synthetic_and_contract_valid(): + fixture = json.loads(FIXTURE_PATH.read_text(encoding="utf-8")) + + assert fixture["fixture_metadata"]["synthetic"] is True + assert fixture["fixture_metadata"]["real_dataset"] is False + normalized = [validate_question(item) for item in fixture["questions"]] + assert {question.split for question in normalized} == {"A", "B"} diff --git a/tests/test_evidence.py b/tests/test_evidence.py new file mode 100644 index 0000000..9c13679 --- /dev/null +++ b/tests/test_evidence.py @@ -0,0 +1,117 @@ +import json +from pathlib import Path + +import pytest + +from awareliquid.evidence import ( + REQUIRED_PROVENANCE_FIELDS, + Evidence, + normalize_evidence, +) + + +FIXTURE = Path(__file__).parent / "fixtures" / "evidence_synthetic.json" + + +def _records(): + fixture = json.loads(FIXTURE.read_text(encoding="utf-8")) + return [ + {**record, "parser_version": fixture["parser_version"]} + for record in fixture["records"] + ] + + +def test_synthetic_provenance_preserves_all_required_boundary_fields(): + evidence = normalize_evidence(_records()[0]) + + assert evidence.page == 2 + assert (evidence.char_start, evidence.char_end) == (41, 66) + assert evidence.table_id == "table-2" + assert evidence.row_id == "row-revenue" + assert evidence.column_ids == ("metric", "amount", "unit") + assert evidence.unit == "CNY million" + assert evidence.footnote == "Amounts exclude tax." + assert evidence.parent_evidence_id == "evidence-parent-fixture" + assert evidence.neighbor_evidence_ids == ( + "evidence-before-fixture", + "evidence-after-fixture", + ) + assert evidence.parse_warning == ("table-grid-reconstructed",) + + serialized = evidence.to_dict() + required = { + "domain", "doc_id", "page", "source_path", "char_start", "char_end", + "section", "title", "table_id", "row_id", "column_ids", "unit", + "footnote", "parent_evidence_id", "neighbor_evidence_ids", "parse_warning", + } + assert required <= serialized.keys() + + +def test_evidence_id_is_stable_and_independent_of_boundary_annotations(): + record = _records()[0] + first = Evidence.from_dict(record) + second = Evidence.from_dict(dict(record)) + annotated = Evidence.from_dict({**record, "parse_warning": ["different-warning"]}) + + assert first.evidence_id == second.evidence_id == annotated.evidence_id + assert first.evidence_id == first.stable_id() + + changed_content = Evidence.from_dict({**record, "content": record["content"] + "!"}) + changed_offset = Evidence.from_dict({**record, "char_end": 67}) + assert changed_content.evidence_id != first.evidence_id + assert changed_offset.evidence_id != first.evidence_id + + +def test_empty_pages_and_parse_warnings_are_retained_without_inference(): + empty_page = Evidence.from_dict(_records()[1]) + + assert empty_page.content == "" + assert empty_page.char_start == empty_page.char_end == 67 + assert empty_page.parse_warning == ("empty-page", "ocr-unavailable") + + +@pytest.mark.parametrize("field", REQUIRED_PROVENANCE_FIELDS) +def test_missing_required_provenance_or_boundary_field_fails_closed(field): + record = _records()[0] + del record[field] + + with pytest.raises(ValueError, match=rf"missing required evidence fields: .*{field}"): + Evidence.from_dict(record) + + +def test_explicit_null_optional_boundaries_are_preserved(): + empty_page = Evidence.from_dict(_records()[1]) + + serialized = empty_page.to_dict() + for field in ( + "section", + "title", + "table_id", + "row_id", + "unit", + "footnote", + "parent_evidence_id", + ): + assert serialized[field] is None + assert serialized["column_ids"] == [] + assert serialized["neighbor_evidence_ids"] == [] + + +@pytest.mark.parametrize("field", ("column_ids", "neighbor_evidence_ids", "parse_warning")) +def test_explicit_null_for_required_collection_boundary_fails_closed(field): + with pytest.raises(TypeError, match=rf"{field} must be a sequence of strings"): + Evidence.from_dict({**_records()[0], field: None}) + + +@pytest.mark.parametrize( + "record, error", + [ + ({"domain": "x"}, "missing required evidence fields"), + ({**_records()[0], "page": 0}, "page must be a positive integer"), + ({**_records()[0], "char_start": 8, "char_end": 7}, "offsets must satisfy"), + ({**_records()[0], "column_ids": "metric"}, "column_ids must be a sequence"), + ], +) +def test_malformed_provenance_fails_closed(record, error): + with pytest.raises((TypeError, ValueError), match=error): + Evidence.from_dict(record) diff --git a/tests/test_evidence_index.py b/tests/test_evidence_index.py new file mode 100644 index 0000000..863dbfa --- /dev/null +++ b/tests/test_evidence_index.py @@ -0,0 +1,68 @@ +from awareliquid.adapter.evidence_index import build_evidence_nodes +from awareliquid.adapter.qa_agent import _merge_evidence_blocks +from awareliquid.memory.lexical_store import LexicalKnowledgeMemory + + +def test_evidence_nodes_keep_structure_and_source_anchors(): + text = "第七条 现金价值\n2023 年金额为 124.5 亿元。\f第三章\n免责等待期。" + nodes = build_evidence_nodes("policy", text, max_chars=80, overlap_chars=0) + + assert len(nodes) == 2 + assert nodes[0].page == 1 + assert nodes[0].clause_id == "第七条" + assert "2023 年" in nodes[0].anchors + assert "124.5 亿元" in nodes[0].anchors + assert nodes[0].neighbor_chunk_idxs == (1,) + assert nodes[1].page == 2 + + +def test_decimal_values_are_not_mistaken_for_clause_ids(): + nodes = build_evidence_nodes( + "report", + "研发投入占比为 8.44%。\n2.2.2 免赔额\f第七条 保险责任。", + ) + clause_ids = {node.clause_id for node in nodes} + assert "8.44" not in clause_ids + assert "2.2.2" in clause_ids + assert "第七条" in clause_ids + + +def test_table_nodes_keep_row_and_column_fields(): + nodes = build_evidence_nodes( + "report", + "| 指标 | 2023 | 2024 |\n| 营业收入 | 100 | 120 |", + ) + + assert nodes[0].table_id == "report:page-1" + assert nodes[0].row_id == "指标 营业收入" + assert nodes[0].column_ids == ("2023", "2024") + + +def test_lexical_store_expands_only_source_neighbors(): + store = LexicalKnowledgeMemory(":memory:") + first = { + "doc_id": "policy", + "chunk_idx": 0, + "neighbor_chunk_idxs": [1], + } + second = {"doc_id": "policy", "chunk_idx": 1, "neighbor_chunk_idxs": [0]} + other = {"doc_id": "other", "chunk_idx": 1, "neighbor_chunk_idxs": []} + first_id = store.write("核心条款", meta=first) + store.write("相邻例外条件", meta=second) + store.write("无关文档", meta=other) + + hits = [(first_id, "核心条款", first)] + expanded = store.expand_neighbors(hits, max_extra=2) + contents = [content for _row_id, content, _meta in expanded] + + assert contents == ["核心条款", "相邻例外条件"] + + +def test_evidence_merge_deduplicates_and_respects_budget(): + merged = _merge_evidence_blocks( + ["[source doc_id=d]\n同一证据", "[source doc_id=d]\n同一证据", "新增证据"], + max_chars=30, + ) + + assert merged.count("同一证据") == 1 + assert len(merged) <= 30 diff --git a/tests/test_experiment_protocol.py b/tests/test_experiment_protocol.py new file mode 100644 index 0000000..d1fc7be --- /dev/null +++ b/tests/test_experiment_protocol.py @@ -0,0 +1,99 @@ +"""tests/test_experiment_protocol.py — E0 协议固化契约. + +真实案例回放（事故 → 协议 → 测试，三条用例对应本仓库实际发生过的事故形态）： + • README bench 表格单跑一遍的 6 题计数（如 lexical recall 5/6）—— + 属"单 seed 单次运行"，引用前必须被 seeds 门槛拦下 + • chance/满分两团的双峰 per-seed 分布 → 必须被判双峰 + • 6/6 全胜对照 → 配对符号检验 p = 0.03125 < 0.05，是"可引用"的最小形态 +""" + +import pytest + +from benchmarks.experiment_protocol import ( + ProtocolViolation, bimodality_1d, fisher_exact_2x2, forbid_bimodal, + paired_sign_test, publishable, require_min_seeds, +) + + +def test_min_seeds_gate(): + # README bench 表格即此类：单次运行的计数不得直接引用 + with pytest.raises(ProtocolViolation, match="单/双 seed"): + require_min_seeds([5 / 6], what="bench-lexical-recall") + with pytest.raises(ProtocolViolation): + require_min_seeds([0.5, 0.6]) # 双 seed 同样不够 + assert require_min_seeds([0.5, 0.6, 0.55]) == [0.5, 0.6, 0.55] + + +def test_bimodality_detects_chance_or_perfect(): + # 两个 seed 卡在 chance 区, 三个 seed 满分 —— 中间是空带, 不得引用 + bm = bimodality_1d([0.17, 0.18, 1.0, 1.0, 1.0]) + assert bm["bimodal"] is True + with pytest.raises(ProtocolViolation, match="双峰"): + forbid_bimodal([0.17, 0.18, 1.0, 1.0, 1.0]) + + +def test_bimodality_passes_unimodal_noise(): + bm = bimodality_1d([0.52, 0.55, 0.53, 0.56, 0.54, 0.55]) + assert bm["bimodal"] is False + + +def test_bimodality_ignores_quantized_neighbors(): + # 量化分数的常态: 簇内全是重复值时类内散度为 0, 任意两个相邻取值的 + # gap_ratio 都会爆炸——2026-09-07 评审修复: 增加绝对间隙下限 0.25, + # 这种正常一致性数据不得误判成双峰 (否则小题集永远过不了门) + assert bimodality_1d([0.75, 0.75, 0.75, 0.875])["bimodal"] is False + assert bimodality_1d([0.875, 0.875, 1.0, 1.0])["bimodal"] is False + # 真双峰 (空带跨 0.25 以上) 仍然要抓 + assert bimodality_1d([0.25, 0.25, 0.75, 0.75])["bimodal"] is True + + +def test_none_per_seed_value_is_violation_not_crash(): + # None 必须以 ProtocolViolation 判负, 不能以 TypeError 崩溃 + with pytest.raises(ProtocolViolation, match="None"): + require_min_seeds([0.5, None, 0.6]) + # publishable 路径: 含 None 的臂记为未过门, 而不是让调用方收到异常 + ok, rep = publishable([0.5, None, 0.6, 0.55], [0.2, 0.3, 0.2, 0.3], + tag="none-arm") + assert not ok and any("None" in r for r in rep["reasons"]) + + +def test_paired_sign_test_significance(): + # 6/6 全胜 → p = 2·(1/64) = 0.03125 < 0.05 + st = paired_sign_test([1.0] * 6, [0.0] * 6) + assert st["p"] == pytest.approx(0.03125) + # 3胜3负 (无平局) → p = 1.0 (不显著) + st2 = paired_sign_test([1, 0.3, 1, 0.3, 1, 0.3], [0.4] * 6) + assert st2["p"] > 0.99 + # 平局被丢弃: 0 vs 0 的对不参与 (3 对里 2 对分胜负) + st3 = paired_sign_test([1, 0, 1], [0, 0, 0]) + assert st3["n_effective"] == 2 and st3["wins"] == 2 + + +def test_fisher_exact_2x2_significant_case(): + # 5/6 vs 0/6 的列联对比 (双侧 Fisher) 必须显著 + r = fisher_exact_2x2([[5, 1], [0, 6]]) + assert r["p"] < 0.05 + + +def test_fisher_exact_independence_edge(): + assert fisher_exact_2x2([[3, 3], [3, 3]])["p"] > 0.9 # 无关联 + + +def test_publishable_full_gate(): + # 干净分离 + 足量 seeds + 单峰 → 可引用 (6/6 全胜 p=0.031) + ok, rep = publishable([1.0] * 6, [0.2] * 6, tag="demo-good") + assert ok, rep["reasons"] + # 4 seeds 全胜: p=2/16=0.125 ≥ 0.05 → 协议正确地拒绝 (4 seeds 不够引用) + ok4s, rep4s = publishable([1.0] * 4, [0.2] * 4, tag="four-seeds") + assert not ok4s and any("配对符号检验" in r for r in rep4s["reasons"]) + # 单次运行 → 只能入档 + ok2, rep2 = publishable([5 / 6], [4 / 6], tag="bench-single-run") + assert not ok2 and any("seed" in r for r in rep2["reasons"]) + # 双峰臂 → 只能入档 + ok3, rep3 = publishable([0.17, 0.18, 1.0, 1.0, 1.0, 1.0], + [0.2] * 6, tag="bimodal-arm") + assert not ok3 and any("双峰" in r for r in rep3["reasons"]) + # 无分离 (p≥α) → 只能入档 + ok4, rep4 = publishable([0.5, 0.6, 0.55, 0.52, 0.58, 0.51], + [0.52, 0.5, 0.58, 0.54, 0.5, 0.55], tag="null") + assert not ok4 and any("配对符号检验" in r for r in rep4["reasons"]) diff --git a/tests/test_formal_reasoning.py b/tests/test_formal_reasoning.py new file mode 100644 index 0000000..74e0a0d --- /dev/null +++ b/tests/test_formal_reasoning.py @@ -0,0 +1,153 @@ +"""Synthetic-only tests for the isolated A4 structural reasoning module.""" + +import ast +from decimal import Decimal +from pathlib import Path + +import pytest + +from awareliquid.evidence import Evidence +from awareliquid.formal_reasoning import ( + EvidenceContext, + EvidenceDecimal, + ExactLocator, + ExactQuery, + OptionState, + evaluate_option, + structural_arithmetic, +) + + +def _evidence(doc_id, page, start, content, **boundaries): + return Evidence( + domain="synthetic", + doc_id=doc_id, + page=page, + source_path=f"/synthetic/{doc_id}.txt", + char_start=start, + char_end=start + len(content), + content=content, + section=boundaries.get("section"), + title=boundaries.get("title"), + table_id=boundaries.get("table_id"), + row_id=boundaries.get("row_id"), + column_ids=boundaries.get("column_ids", ()), + unit=boundaries.get("unit"), + footnote=boundaries.get("footnote"), + parent_evidence_id=boundaries.get("parent_evidence_id"), + neighbor_evidence_ids=boundaries.get("neighbor_evidence_ids", ()), + parse_warning=boundaries.get("parse_warning", ()), + ) + + +def _context(): + later = _evidence( + "doc-z", 2, 30, "Revenue: 12.50 CNY million; exception none.", + section="Clause 2", title="Synthetic report", table_id="table-1", + row_id="revenue", column_ids=("metric", "amount"), unit="CNY million", + footnote="exact synthetic footnote", parent_evidence_id="parent-1", + neighbor_evidence_ids=("before-1",), parse_warning=("synthetic-warning",), + ) + first = _evidence( + "doc-a", 1, 5, "Rating: AA; date 2026-01-01; rate 5%.", + section="Clause 1", title="Synthetic schedule", table_id="table-2", + row_id="rating", column_ids=("rating",), unit="percent", + ) + return EvidenceContext((later, first)) + + +def test_context_keeps_exact_text_and_every_boundary_without_transformation(): + context = _context() + original = context.records[0] + + assert context.records[0] is original + assert context.to_dicts()[0]["content"] == "Revenue: 12.50 CNY million; exception none." + assert context.to_dicts()[0]["section"] == "Clause 2" + assert context.to_dicts()[0]["table_id"] == "table-1" + assert context.to_dicts()[0]["footnote"] == "exact synthetic footnote" + assert context.select((original.evidence_id,)).records == (original,) + + +def test_decimal_arithmetic_is_decimal_only_bound_and_whitelisted(): + context = _context() + left = EvidenceDecimal("12.50", (context.records[0].evidence_id,), context, "CNY") + right = EvidenceDecimal(Decimal("2.50"), (context.records[1].evidence_id,), context, "CNY") + + result = structural_arithmetic("subtract", (left, right)) + + assert result.value == Decimal("10.00") + assert result.evidence_ids == (left.evidence_ids[0], right.evidence_ids[0]) + with pytest.raises(TypeError, match="decimal values"): + EvidenceDecimal(1.5, left.evidence_ids, context) + with pytest.raises(ValueError, match="unknown structural arithmetic"): + structural_arithmetic("unknown", (left,)) + with pytest.raises(ZeroDivisionError): + structural_arithmetic("divide", (left, EvidenceDecimal("0", right.evidence_ids, context))) + + +def test_option_states_are_deterministic_and_evidence_backed(): + context = _context() + actual = EvidenceDecimal("12.50", (context.records[0].evidence_id,), context, "CNY") + expected = EvidenceDecimal("12.50", (context.records[1].evidence_id,), context, "CNY") + + supported = evaluate_option( + "A", actual, expected, observed_text="exception none", required_text="exception none" + ) + refuted = evaluate_option("B", actual, EvidenceDecimal("10", expected.evidence_ids, context, "CNY")) + insufficient = evaluate_option("C", actual, None) + + assert supported.state is OptionState.SUPPORTED + assert supported.evidence_ids == (actual.evidence_ids[0], expected.evidence_ids[0]) + assert refuted.state is OptionState.REFUTED + assert insufficient.state is OptionState.INSUFFICIENT + assert insufficient.evidence_ids == actual.evidence_ids + + +def test_exact_locator_is_a_scoped_and_doc_page_char_stable(): + context = _context() + locator = ExactLocator(context) + + result = locator.locate(ExactQuery(split="A", doc_ids=("doc-z", "doc-a"))) + assert result == (context.records[1].evidence_id, context.records[0].evidence_id) + + record = context.records[0] + exact = locator.locate(ExactQuery( + split="A", doc_ids=("doc-z",), title="Synthetic report", + clause="Clause 2", phrase="Revenue", numbers=("12.50",), + currencies=("CNY",), table_fields=(("row_id", "revenue"), ("column_ids", "amount")), + char_start=record.char_start, char_end=record.char_end, + )) + assert exact == (record.evidence_id,) + assert locator.locate(ExactQuery(split="A", doc_ids=("doc-z",), phrase="Income")) == () + assert locator.locate(ExactQuery(split="A", doc_ids=("doc-a",), phrase="Revenue")) == () + + +def test_locator_fails_closed_for_missing_a_scope_and_unofficial_b_data(): + with pytest.raises(ValueError, match="A queries require"): + ExactQuery(split="A") + with pytest.raises(ValueError, match="official B data"): + ExactQuery(split="B") + with pytest.raises(ValueError, match="must not supply doc_ids"): + ExactQuery(split="B", official_b_data=True, doc_ids=("doc-a",)) + with pytest.raises(ValueError, match="phrase must be a non-empty"): + ExactQuery(split="A", doc_ids=("doc-a",), phrase="") + + +def test_module_has_no_prohibited_imports_or_dynamic_execution_nodes(): + source = Path(__file__).parents[1] / "awareliquid" / "formal_reasoning.py" + tree = ast.parse(source.read_text(encoding="utf-8")) + imported = { + alias.name.split(".")[0].lower() + for node in ast.walk(tree) + if isinstance(node, (ast.Import, ast.ImportFrom)) + for alias in node.names + } + banned_roots = {"torch", "transformers", "sentence_transformers"} + calls = { + node.func.id + for node in ast.walk(tree) + if isinstance(node, ast.Call) and isinstance(node.func, ast.Name) + } + + assert not imported & banned_roots + assert not {"eval", "exec"} & calls diff --git a/tests/test_hybrid.py b/tests/test_hybrid.py index 3f5d54c..24f9a9e 100644 --- a/tests/test_hybrid.py +++ b/tests/test_hybrid.py @@ -42,6 +42,13 @@ def test_bm25_respects_doc_id_filter(): assert s.search_bm25("信用评级", doc_ids=["ann"]) == [] +def test_vector_store_empty_doc_filter_is_not_global(): + enc, s = _store() + s.write(enc.encode("信用评级为 AAA"), "信用评级为 AAA", meta={"doc_id": "bond"}) + assert s.query(enc.encode("信用评级"), doc_ids=[]) == [] + assert s.search_bm25("信用评级", doc_ids=[]) == [] + + # ---- RRF fusion --------------------------------------------------------- def test_rrf_fuse_combines_and_dedupes(): diff --git a/tests/test_lexical_store.py b/tests/test_lexical_store.py new file mode 100644 index 0000000..34d3dca --- /dev/null +++ b/tests/test_lexical_store.py @@ -0,0 +1,104 @@ +from awareliquid.adapter.qwen_client import MockChatClient +from awareliquid.adapter.qa_agent import MemoryQAAgent, RetrievalConfig +from awareliquid.memory.lexical_store import LexicalKnowledgeMemory + + +def _lexical_agent(): + return MemoryQAAgent( + chat_client=MockChatClient(), + config=RetrievalConfig( + retrieval_backend="lexical", + max_chars=120, + overlap_chars=10, + top_k=3, + ), + db_path=":memory:", + ) + + +def test_lexical_store_matches_two_character_chinese_terms_and_metadata(): + store = LexicalKnowledgeMemory(":memory:") + store.write( + "本产品利率为 3.25%，按季度调整。", + meta={"doc_id": "rates", "section": "贷款利率", "page": 4}, + ) + store.write( + "本年度保费收入增长 8.1%。", + meta={"doc_id": "insurance", "section": "保费收入", "page": 8}, + ) + + rate_hits = store.search_bm25("利率") + assert rate_hits and rate_hits[0][1].startswith("本产品利率") + assert store.search_bm25("利率", doc_ids=["insurance"]) == [] + assert store.search_bm25("保费", doc_ids=["insurance"]) + assert store.available_doc_ids() == ["insurance", "rates"] + + +def test_bm25_structural_bonus_prefers_exact_amount_anchor(): + store = LexicalKnowledgeMemory(":memory:") + store.write("营业收入和利润均已披露。", meta={"doc_id": "generic"}) + store.write("2023 年营业收入为 124.5 亿元。", meta={"doc_id": "exact"}) + + hits = store.search_bm25("2023 年营业收入 124.5 亿元", top_k=2) + + assert hits[0][2]["doc_id"] == "exact" + + +def test_fielded_bm25_prioritizes_table_row_metadata(): + store = LexicalKnowledgeMemory(":memory:") + store.write( + "现金及现金等价物。", + meta={"doc_id": "generic", "row_id": "资产负债表"}, + ) + store.write( + "53,345,930", + meta={"doc_id": "cashflow", "row_id": "经营活动产生的现金流量净额"}, + ) + + hits = store.search_bm25("经营现金流量净额", top_k=2) + + assert len(hits) == 2 + assert hits[0][2]["doc_id"] == "cashflow" + + +def test_explicit_empty_doc_filter_never_means_all_documents(): + store = LexicalKnowledgeMemory(":memory:") + store.write("营业收入为 124.5 亿元。", meta={"doc_id": "annual"}) + assert store.search_bm25("营业收入") + assert store.search_bm25("营业收入", doc_ids=[]) == [] + + +def test_agent_lexical_backend_does_not_construct_an_encoder(): + agent = _lexical_agent() + assert agent.encoder is None + agent.ingest_document("annual", "2023 年营业收入为 124.5 亿元，利率为 3.25%。") + agent.ingest_document("other", "本年度保费收入增长 8.1%。") + + hits = agent.retrieve("利率是多少？", doc_ids=["annual"]) + assert hits and "3.25%" in hits[0] + assert agent.retrieve("保费是多少？", doc_ids=["annual"]) == [] + assert agent.retrieve("营业收入是多少？", doc_ids=[]) == [] + + +def test_lexical_agent_can_answer_offline_without_qwen_key(): + agent = _lexical_agent() + agent.ingest_document("annual", "2023 年营业收入为 124.5 亿元。") + result = agent.answer_question( + qid="q1", + question="2023 年营业收入是多少？", + options=["114.9 亿元", "124.5 亿元"], + doc_ids=["annual"], + ) + assert result.answer in {"A", "B"} + assert result.usage.total_tokens > 0 + + +def test_lexical_agent_requires_explicit_mock_without_api_key(monkeypatch): + monkeypatch.delenv("AWARELIQUID_LLM_API_KEY", raising=False) + monkeypatch.delenv("DASHSCOPE_API_KEY", raising=False) + monkeypatch.setenv("AWARELIQUID_LLM_BACKEND", "mock") + monkeypatch.setenv("AWARELIQUID_TEST_MODE", "1") + agent = MemoryQAAgent( + config=RetrievalConfig(retrieval_backend="lexical"), + ) + assert isinstance(agent.chat_client, MockChatClient) diff --git a/tests/test_qwen_client.py b/tests/test_qwen_client.py index 3589ed0..2ca72c6 100644 --- a/tests/test_qwen_client.py +++ b/tests/test_qwen_client.py @@ -1,12 +1,52 @@ -import os +import json +from pathlib import Path + +import pytest from awareliquid.adapter.qwen_client import ( + DEFAULT_TOKEN_BUDGET, MockChatClient, + QwenChatClient, TokenUsage, + UsageLedger, build_chat_client, + formal_network_denied_probe, ) +class _Response: + def __init__(self, body): + self.body = body + + def read(self): + return json.dumps(self.body).encode("utf-8") + + def __enter__(self): + return self + + def __exit__(self, *unused): + return False + + +def _response(model="qwen-plus", usage=None): + return { + "model": model, + "choices": [{"message": {"content": "A"}}], + "usage": usage or {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5}, + } + + +def _formal_client(tmp_path, **kwargs): + ledger_path = tmp_path / "usage.json" + return QwenChatClient( + api_key="test-key", + competition_mode=True, + formal_run_id=f"test-run-{tmp_path.name}", + formal_ledger_path=str(ledger_path), + **kwargs, + ) + + def test_token_usage_add(): a = TokenUsage(10, 2, 12) b = TokenUsage(5, 1, 6) @@ -22,14 +62,260 @@ def test_mock_client_reports_usage_and_answers(): assert res.usage.total_tokens == res.usage.prompt_tokens + res.usage.completion_tokens -def test_build_falls_back_to_mock_without_key(monkeypatch): +def test_missing_key_fails_closed_without_mock_fallback(monkeypatch): + monkeypatch.setattr("urllib.request.urlopen", lambda *_args, **_kwargs: pytest.fail("transport called")) monkeypatch.delenv("AWARELIQUID_LLM_API_KEY", raising=False) monkeypatch.delenv("DASHSCOPE_API_KEY", raising=False) monkeypatch.setenv("AWARELIQUID_LLM_BACKEND", "qwen") - client = build_chat_client() - assert isinstance(client, MockChatClient) + with pytest.raises(RuntimeError, match="API_KEY"): + build_chat_client() def test_build_respects_explicit_mock(monkeypatch): monkeypatch.setenv("AWARELIQUID_LLM_BACKEND", "mock") - assert isinstance(build_chat_client(), MockChatClient) + with pytest.raises(RuntimeError, match="explicit test mode"): + build_chat_client() + assert isinstance(build_chat_client(test_mode=True), MockChatClient) + + +def test_formal_network_requires_explicit_opt_in(monkeypatch, tmp_path): + monkeypatch.setenv("AWARELIQUID_ALLOW_FORMAL_NETWORK", "1") + monkeypatch.setattr( + "urllib.request.urlopen", + lambda *_args, **_kwargs: _Response(_response()), + ) + + result = _formal_client(tmp_path).chat( + [{"role": "user", "content": "Answer A"}], max_tokens=8 + ) + + assert result.text == "A" + assert result.usage.total_tokens == 5 + + +def test_usage_ledger_reserves_reconciles_and_persists(tmp_path): + path = tmp_path / "usage.json" + ledger = UsageLedger(max_tokens=100, path=str(path)) + reserved = ledger.reserve(30) + ledger.reconcile(reserved, TokenUsage(10, 5, 15)) + assert ledger.snapshot() == { + "used_tokens": 15, + "reserved_tokens": 0, + "remaining_tokens": 85, + "max_tokens": 100, + } + reopened = UsageLedger(max_tokens=100, path=str(path)) + assert reopened.snapshot()["used_tokens"] == 15 + + +def test_shared_persistent_ledger_reconciles_stale_clients_atomically(tmp_path): + path = tmp_path / "shared-usage.json" + # Both ledgers start from the same empty file. The second therefore has a + # deliberately stale in-memory balance when the first commits its usage. + first = UsageLedger(max_tokens=DEFAULT_TOKEN_BUDGET, path=str(path)) + second = UsageLedger(max_tokens=DEFAULT_TOKEN_BUDGET, path=str(path)) + + first_reservation = first.reserve(0) + second_reservation = second.reserve(0) + first.reconcile(first_reservation, TokenUsage(2_000_000, 500_000, 2_500_000)) + second.reconcile(second_reservation, TokenUsage(2_000_000, 500_000, 2_500_000)) + + assert json.loads(path.read_text(encoding="utf-8")) == { + "max_tokens": DEFAULT_TOKEN_BUDGET, + "used_tokens": DEFAULT_TOKEN_BUDGET, + } + assert first.snapshot()["used_tokens"] == DEFAULT_TOKEN_BUDGET + + # A third stale instance must refresh under the same lock and reject an + # over-budget aggregate rather than overwrite the committed 5M balance. + third = UsageLedger(max_tokens=DEFAULT_TOKEN_BUDGET, path=str(path)) + third_reservation = third.reserve(0) + with pytest.raises(RuntimeError, match="budget exceeded"): + third.reconcile(third_reservation, TokenUsage(1, 0, 1)) + third.release(third_reservation) + assert json.loads(path.read_text(encoding="utf-8"))["used_tokens"] == DEFAULT_TOKEN_BUDGET + + +@pytest.mark.parametrize( + ("kwargs", "message"), + [ + ({"model": "qwen-unapproved"}, "allowlisted"), + ({"base_url": "http://dashscope.aliyuncs.com/compatible-mode/v1"}, "HTTPS"), + ], +) +def test_formal_config_denials_happen_before_transport(tmp_path, monkeypatch, kwargs, message): + monkeypatch.setattr("urllib.request.urlopen", lambda *_args, **_kwargs: pytest.fail("transport called")) + with pytest.raises(ValueError, match=message): + _formal_client(tmp_path, **kwargs) + + +def test_formal_config_accepts_third_party_https_qwen_endpoint(tmp_path): + client = _formal_client( + tmp_path, + provider="third-party-gateway", + base_url="https://gateway.example.invalid/openai/v1", + ) + assert client.provider == "third-party-gateway" + assert client.base_url == "https://gateway.example.invalid/openai/v1" + + +def test_formal_config_accepts_official_qwen36_plus_model(tmp_path): + client = _formal_client(tmp_path, model="qwen3.6-plus") + assert client.model == "qwen3.6-plus" + + +def test_formal_mode_requires_persistent_ledger(): + with pytest.raises(ValueError, match="formal_ledger_path"): + QwenChatClient(api_key="test-key", competition_mode=True) + + +@pytest.mark.parametrize( + "ledger_budget", + [DEFAULT_TOKEN_BUDGET - 1, DEFAULT_TOKEN_BUDGET + 1], +) +def test_formal_mode_rejects_prebuilt_ledger_with_noncanonical_budget(tmp_path, ledger_budget): + ledger = UsageLedger(max_tokens=ledger_budget, path=str(tmp_path / "usage.json")) + with pytest.raises(ValueError, match="exact 5000000 token budget"): + QwenChatClient( + api_key="test-key", + competition_mode=True, + usage_ledger=ledger, + formal_run_id=f"test-run-{tmp_path.name}", + formal_ledger_path=str(tmp_path / "usage.json"), + ) + + +def test_formal_mode_rejects_noncanonical_token_budget(tmp_path): + with pytest.raises(ValueError, match="exact 5000000 token budget"): + QwenChatClient( + api_key="test-key", + competition_mode=True, + token_budget=DEFAULT_TOKEN_BUDGET - 1, + formal_run_id=f"test-run-{tmp_path.name}", + formal_ledger_path=str(tmp_path / "usage.json"), + ) + + +def test_formal_clients_share_one_explicit_run_ledger_and_aggregate(tmp_path): + run_id = f"aggregate-{tmp_path.name}" + ledger_path = str(tmp_path / "run-usage.json") + first = QwenChatClient( + api_key="test-key", + competition_mode=True, + formal_run_id=run_id, + formal_ledger_path=ledger_path, + ) + second = QwenChatClient( + api_key="test-key", + competition_mode=True, + formal_run_id=run_id, + formal_ledger_path=ledger_path, + ) + first_reservation = first.usage_ledger.reserve(0) + second_reservation = second.usage_ledger.reserve(0) + first.usage_ledger.reconcile(first_reservation, TokenUsage(2_000_000, 500_000, 2_500_000)) + second.usage_ledger.reconcile(second_reservation, TokenUsage(2_000_000, 500_000, 2_500_000)) + assert second.usage_ledger.snapshot()["used_tokens"] == DEFAULT_TOKEN_BUDGET + + third = QwenChatClient( + api_key="test-key", + competition_mode=True, + formal_run_id=run_id, + formal_ledger_path=ledger_path, + ) + reservation = third.usage_ledger.reserve(0) + with pytest.raises(RuntimeError, match="budget exceeded"): + third.usage_ledger.reconcile(reservation, TokenUsage(1, 0, 1)) + third.usage_ledger.release(reservation) + + +def test_formal_run_rejects_an_alternate_ledger_path(tmp_path): + run_id = f"alternate-{tmp_path.name}" + QwenChatClient( + api_key="test-key", + competition_mode=True, + formal_run_id=run_id, + formal_ledger_path=str(tmp_path / "first.json"), + ) + with pytest.raises(ValueError, match="already bound"): + QwenChatClient( + api_key="test-key", + competition_mode=True, + formal_run_id=run_id, + formal_ledger_path=str(tmp_path / "alternate.json"), + ) + + +def test_formal_network_probe_denies_without_urlopen_or_persistent_io(tmp_path, monkeypatch): + def sentinel(*_args, **_kwargs): + pytest.fail("formal network denial probe reached urllib") + + monkeypatch.setattr("urllib.request.urlopen", sentinel) + monkeypatch.setattr("awareliquid.adapter.qwen_client.UsageLedger", lambda *args: pytest.fail("ledger initialized")) + before = sorted(path.relative_to(tmp_path) for path in tmp_path.rglob("*")) + formal_network_denied_probe() + after = sorted(path.relative_to(tmp_path) for path in tmp_path.rglob("*")) + assert after == before + + +def test_formal_call_is_denied_before_transport(tmp_path, monkeypatch): + client = _formal_client(tmp_path) + monkeypatch.setattr("urllib.request.urlopen", lambda *_args, **_kwargs: pytest.fail("transport called")) + monkeypatch.setattr("urllib.request.Request", lambda *_args, **_kwargs: pytest.fail("request constructed")) + with pytest.raises(RuntimeError, match="network transport is denied"): + client.chat([{"role": "user", "content": "offline only"}], max_tokens=1) + assert client.usage_ledger.snapshot()["used_tokens"] == 0 + + +@pytest.mark.parametrize( + "usage", + [ + None, + {"prompt_tokens": 3, "completion_tokens": 2}, + {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 4}, + {"prompt_tokens": "3", "completion_tokens": 2, "total_tokens": 5}, + ], +) +def test_qwen_usage_must_be_known_complete_and_exact(tmp_path, monkeypatch, usage): + client = QwenChatClient( + api_key="test-key", + usage_ledger_path=str(tmp_path / "usage.json"), + ) + calls = [] + + def fake_urlopen(request, timeout): + calls.append(request.full_url) + body = _response(usage=usage) + if usage is None: + body.pop("usage") + return _Response(body) + + monkeypatch.setattr("urllib.request.urlopen", fake_urlopen) + with pytest.raises(RuntimeError, match="usage"): + client.chat([{"role": "user", "content": "offline only"}], max_tokens=1) + assert calls == ["https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"] + assert client.usage_ledger.snapshot()["used_tokens"] == 0 + + +def test_qwen_call_persists_exact_usage(tmp_path, monkeypatch): + client = QwenChatClient( + api_key="test-key", + usage_ledger_path=str(tmp_path / "usage.json"), + ) + monkeypatch.setattr("urllib.request.urlopen", lambda request, timeout: _Response(_response())) + result = client.chat([{"role": "user", "content": "offline only"}], max_tokens=1) + assert result.usage == TokenUsage(3, 2, 5) + assert json.loads(Path(client.usage_ledger.path).read_text(encoding="utf-8")) == { + "max_tokens": 5_000_000, + "used_tokens": 5, + } + + +def test_qwen_budget_denies_before_transport(tmp_path, monkeypatch): + ledger = UsageLedger(max_tokens=5, path=str(tmp_path / "budget.json")) + reserved = ledger.reserve(0) + ledger.reconcile(reserved, TokenUsage(3, 2, 5)) + spent = QwenChatClient(api_key="test-key", usage_ledger=ledger) + monkeypatch.setattr("urllib.request.urlopen", lambda *unused: pytest.fail("transport called")) + with pytest.raises(RuntimeError, match="budget exhausted"): + spent.chat([{"role": "user", "content": "offline only"}], max_tokens=1) diff --git a/tests/test_submit.py b/tests/test_submit.py index c975830..284367d 100644 --- a/tests/test_submit.py +++ b/tests/test_submit.py @@ -1,54 +1,77 @@ -"""Unit tests for the submission builder's input parsing (pure helpers).""" +"""Focused local tests for the pure official submission renderer.""" +import csv +import io import json import sys from pathlib import Path -sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) # repo root for `submit` +import pytest + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) import submit # noqa: E402 +from awareliquid.adapter.afac_contract import ContractError, SubmissionAnswer, parse_questions + + +def test_render_csv_delegates_to_the_official_five_column_contract(): + questions = _questions() + + output = submit.render_csv(questions, _answers()) + + assert list(csv.reader(io.StringIO(output))) == [ + ["qid", "answer", "prompt_tokens", "completion_tokens", "total_tokens"], + ["summary", "", "3", "2", "5"], + ["q1", "A", "3", "2", "5"], + ] -def test_options_dict_ordered_by_letter(): - # A {letter: text} dict must become a list in A,B,C,D order so the agent's - # positional labels line up with the official option letters. - opts = {"C": "third", "A": "first", "B": "second", "D": "fourth"} - assert submit.options_to_list(opts) == ["first", "second", "third", "fourth"] +def test_render_csv_does_not_accept_partial_outputs_or_unused_token_fields(): + with pytest.raises(ContractError) as caught: + submit.render_csv(_questions(), []) + assert caught.value.code == "INCOMPLETE_OR_UNKNOWN_QIDS" + assert "unused_tokens" not in submit.render_csv(_questions(), _answers()) -def test_options_list_passthrough(): - assert submit.options_to_list(["x", "y"]) == ["x", "y"] - assert submit.options_to_list(None) == [] +def test_options_to_list_preserves_official_option_order(): + assert submit.options_to_list({"C": "third", "A": "first", "B": "second", "D": "fourth"}) == [ + "first", "second", "third", "fourth", + ] -def test_answer_format_maps_to_qtype(): - assert submit._QTYPE["mcq"] == "mcq" - assert submit._QTYPE["multiple_choice"] == "multi" - assert submit._QTYPE["判断"] == "tf" - assert submit._QTYPE["true_false"] == "tf" +def test_checkpoint_is_atomic_ordered_and_input_bound(tmp_path): + questions = _questions() + payloads = [{ + "qid": "q1", "domain": "synthetic", "split": "A", + "question": "synthetic", "options": {"A": "a", "B": "b", "C": "c", "D": "d"}, + "answer_format": "mcq", "type": "synthetic", "doc_ids": ["doc"], + }] + docs = {"doc": "source"} + checkpoint = tmp_path / "run.checkpoint.json" + answers = _answers() + submit._atomic_write_text( + checkpoint, + json.dumps(submit._checkpoint_payload(payloads, docs, "lexical", answers)), + ) -def test_load_questions_jsonl_and_array(tmp_path): - items = [{"qid": "1", "question": "a"}, {"qid": "2", "question": "b"}] - jsonl = tmp_path / "q.jsonl" - jsonl.write_text("\n".join(json.dumps(x, ensure_ascii=False) for x in items), encoding="utf-8") - assert submit.load_questions(str(jsonl)) == items + restored = submit._load_checkpoint(checkpoint, payloads, questions, docs, "lexical") + assert restored == answers + assert not list(tmp_path.glob("*.tmp")) - arr = tmp_path / "q.json" - arr.write_text(json.dumps(items, ensure_ascii=False), encoding="utf-8") - assert submit.load_questions(str(arr)) == items + with pytest.raises(ValueError, match="question set"): + submit._load_checkpoint(checkpoint, [dict(payloads[0], qid="other")], questions, docs, "lexical") -def test_load_docs_dict_and_array_and_dir(tmp_path): - d1 = tmp_path / "docs.json" - d1.write_text(json.dumps({"a": "text-a"}, ensure_ascii=False), encoding="utf-8") - assert submit.load_docs(str(d1)) == {"a": "text-a"} +def _questions(): + return parse_questions( + [{ + "qid": "q1", "domain": "synthetic", "split": "A", + "question": "synthetic", "options": {"A": "a", "B": "b", "C": "c", "D": "d"}, + "answer_format": "mcq", "type": "synthetic", "doc_ids": ["doc"], + }] + ) - d2 = tmp_path / "docs2.json" - d2.write_text(json.dumps([{"doc_id": "b", "content": "text-b"}], ensure_ascii=False), encoding="utf-8") - assert submit.load_docs(str(d2)) == {"b": "text-b"} - ddir = tmp_path / "docs_dir" - ddir.mkdir() - (ddir / "c.txt").write_text("text-c", encoding="utf-8") - assert submit.load_docs(str(ddir)) == {"c": "text-c"} +def _answers(): + return [SubmissionAnswer("q1", "A", 3, 2, 5)]