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Character Identity Protocol — Documentation

This documentation covers the Character Identity Protocol (CIP), an inference-time adoption-governance framework for validating, adopting, rejecting, purging, re-binding, and re-converging character identity outputs in probabilistic generative systems.

CIP does not attempt to control the model directly. It governs the workflow conditions under which reconstructed outputs are evaluated before adoption.

→ For the project overview and research context, see the GitHub README

Who This Documentation Is For

This documentation may be useful for:

General users
People working with generative image systems who want to understand why characters change across generations and how to manage identity continuity.

Researchers
People studying identity drift, probabilistic reconstruction behavior, and inference-time governance in generative systems.

Governance and operational teams
People assessing CIP for enterprise deployment, audit-ready workflows, failure handling, or reproducibility requirements.

Start Here by Goal

Goal Start with
Understand how generative AI works How Generative AI Actually Behaves
Write better prompts A Simple Structure for Writing Prompts
Understand why characters change Character Identity Drift
Try CIP immediately Getting Started → Quickstart
Understand Hard Abort and recovery Quality Gate & Hard Abort → Re-Convergence
Use an audit-ready session template Protocol Template
Read the theory and specification Technical Mechanism → CIP Spec v0.1
Evaluate for enterprise or governance White Paper → Decision Pack

Research Entry

For readers approaching CIP from a research perspective, the recommended entry sequence is:

Technical Mechanism → Character Drift Taxonomy → CIP Specification v0.1 → White Paper

These documents cover the operational model, drift classification, normative requirements, and the theoretical framework.

1. Basic Understanding

2. Prompting and Input Design

3. Understanding the Problem

4. Entering CIP

5. Theory and Specification

6. Governance Workflow

7. Extensions and Reference

8. Case Studies

Licensed under CC BY 4.0 — 2026