CIP Complete Specification (v1.0)
This document defines the closed-loop control architecture of the Character Identity Protocol. Threshold values (ε, θ) are intentionally undefined and represent tunable, domain-specific parameters.
Overview
Character Identity Protocol (CIP) is a closed-loop control system designed to stabilize identity in probabilistic generative systems.
The system operates on the principle that generative models do not solve a given input directly, but reconstruct it into an internal representation before generating output.
Operational Objective
CIP is not designed to improve generation quality.
Its purpose is to ensure:
- Identity consistency — outputs converge to a defined identity state
- Reproducibility of outputs — generation cycles produce auditable, verifiable results
- Operational controllability — the generation process is governed by explicit rules
- Failure detectability — drift and collapse states are detected and terminated
CIP transforms generative systems from uncontrolled probabilistic processes into manageable and governable systems.
This makes CIP applicable not only to creative workflows, but also to enterprise environments requiring risk control, auditability, and identity assurance.
Level 0 — Framework
CIP (Character Identity Protocol)
A multi-layer system for controlling, observing, restoring, and terminating identity states.
| Layer | Function |
|---|---|
| Level −1 | Operational Objective (governance purpose) |
| Level 0 | Framework (CIP structure) |
| Level 1 | Reconstruction Model (A → A′ → B′) |
| Level 2 | Control Target (A′) |
| Level 3 | Control Theory (Anchor Model) |
| Level 4 | Anchor Re-Convergence Method |
| Level 4.5 | Observation & Evaluation |
| Level 5 | Safety Mechanism (Hard Abort / Rollback) |
Level 1 — Reconstruction Model
A → A′ → B′
| Symbol | Definition |
|---|---|
| A | User input (intended instruction) |
| A′ | Internally reconstructed problem representation |
| B′ | Generated output based on A′ |
| B | Intended output (target) |
Definition
Generative AI does not solve A. It reconstructs A into A′ and solves A′, producing B′.
This reconstruction process is the primary cause of identity drift.
Observables
- Δ₁ = distance(A, A′)
- Δ₂ = distance(B, B′)
A′ is not directly observable and must be inferred from B′.
State Boundaries
| Condition | State |
|---|---|
| Δ₂ < ε₁ | Stable |
| ε₁ ≤ Δ₂ < ε₂ | Drift |
| Δ₂ ≥ ε₂ | Collapse |
Level 2 — Control Target
A′ (Reconstructed Problem)
Definition
A′ = f(A, Context, Optimization Pressure)
Internal Structure of A′ (Conceptual)
A′ consists of multiple latent components:
- Identity — character, face, personality
- Structure — pose, composition
- Style — rendering, visual regime
- Contextual constraints — session context, environmental signals
Drift may occur independently in each component. Control mechanisms must target these components explicitly.
Control Target Clarification
All control mechanisms operate on A′, not A.
- Anchor constrains the reconstruction space of A′
- Control rules limit degrees of freedom in A′ transformation
- Re-convergence modifies A′ toward anchor-constrained regions
Therefore, A′ is the sole controllable entity in the system.
States
| State | Description |
|---|---|
| S₀ | Stable |
| S₁ | Drifting |
| S₂ | Collapsed |
Observables
- Consistency of output
- Instruction retention rate
- Unintended variation
State Transition Rules
| Condition | Transition |
|---|---|
| Drift Rate > θ₁ | → S₁ |
| Drift Rate > θ₂ | → S₂ |
Level 3 — Control Theory
Anchor Model
Anchor = Low-entropy reference that constrains A′ reconstruction
Control Rules
Single Command Constraint Only one instruction per execution.
Single State Transition Only one state dimension may change per step.
Transition Decomposition Large changes must be decomposed into smaller steps.
Observables
- Anchor retention rate
- Instruction deviation rate
- State change magnitude
Boundary Condition
| Condition | Action Required |
|---|---|
| Anchor deviation > θ₃ | Re-convergence required |
Level 4 — Anchor Re-Convergence Method
Definition
A method to restore A′ to an anchor-constrained state after drift.
Procedure
- Reintroduce anchor
- Remove conflicting conditions
- Execute single-command generation
- Validate output
Conditions
| Condition | Result |
|---|---|
| Δ₂ < ε₁ | Success |
| Re-convergence fails after multiple attempts and Δ₂ ≥ ε₂ | Failure → Level 5 |
Level 4.5 — Observation & Evaluation
Metrics
| Metric | Description |
|---|---|
| Identity Score | Similarity of output identity to anchor |
| Consistency Score | Stability of identity across generations |
| Drift Score | Magnitude of deviation from anchor state |
State Classification
| Score | State |
|---|---|
| ≥ 0.9 | Stable |
| 0.7 – 0.9 | Drift |
| < 0.7 | Collapse |
Decision Logic
State = argmax(State Probability)
Level 5 — Safety Mechanism
Hard Abort
Immediate termination when collapse is detected.
Trigger Conditions
| Condition | Action |
|---|---|
| State = Collapse | Hard Abort |
| Drift Score > θ₂ | Hard Abort |
Rollback
- Return to last known stable state
- Restart from anchor-based input
System Summary
CIP is a closed-loop system that:
- Controls A′ through anchor constraints and single-command rules
- Observes A′ through identity, consistency, and drift scoring
- Restores A′ through anchor re-convergence procedures
- Terminates invalid states through Hard Abort and rollback
See also: Technical Mechanism — CIP Specification v0.1 — Glossary