Governance Interlock Protocol (G.I.P.)

A dynamic pre-generation condition confirmation model for probabilistic constraint compliance in Large Language Models.

Overview

The Governance Interlock Protocol (G.I.P.) is a prompt engineering methodology that probabilistically improves constraint compliance in LLM output generation through a pre-generative and intrinsic approach.

Unlike existing countermeasures based on post-hoc correction, instruction hierarchy training, or critique-and-revision cycles, G.I.P. requires the model to linguistically self-verify the functional state of pre-defined preconditions immediately before response generation—shifting the intervention point to the moment of generation itself.

Key Characteristics

• Pre-generative intervention: Inserts condition confirmation at the point of generation, not after
• Natural language only: No fine-tuning, no external scripts, no API modifications
• Extensibility: Functions across arbitrary condition items beyond the standard three (user profile, persona, bias configuration)
• Audit-independent operation: Effective even when post-hoc audit protocols are removed
• Multi-layered defense: Designed to compose with re-prompting and self-correction layers

Citation

@misc{masahiko_o_2026_gip,
  author       = {Masahiko.O},
  title        = {Governance Interlock Protocol (G.I.P.): Definition and Implementation of a Dynamic Pre-Generation Condition Confirmation Model for Probabilistic Constraint Compliance in Large Language Models},
  year         = {2026},
  doi          = {10.5281/zenodo.19973903},
  url          = {https://doi.org/10.5281/zenodo.19973903},
  note         = {Preprint, originally presented 2026-05-05}
}

• Author: Masahiko.O
• DOI: 10.5281/zenodo.19973903
• License: Creative Commons Attribution 4.0 International (CC-BY-4.0)

Full Paper

• paper.md — Full paper in Markdown format
• paper.pdf — PDF version

Original preprint on Zenodo:

• https://doi.org/10.5281/zenodo.19973903

Related Protocols

This is part of a four-protocol research series on natural-language LLM intervention by Masahiko.O:

• G.I.P. (this repository) — Instruction adherence through pre-generative self-attestation
• CMDP — Probability distribution redistribution for creative output
• PRACT — Persona drift prevention via named-subject attention
• CAP — Internal state articulation through metaphorical translation

Author

Masahiko.O — Independent AI researcher

• GitHub: @Masahiko-O

License

This work is licensed under CC-BY-4.0. You are free to share and adapt the material with proper attribution.