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Synchronization Audit Protocol v0.5.0-candidate — AI Agent Hook / Automatic Synchronization Detection

19 Jun 23:42
@SamuraiWriter7 SamuraiWriter7
v0.5.0-candidate
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Synchronization Audit Protocol v0.5.0-candidate — AI Agent Hook / Automatic Synchronization Detection

This release adds the AI Agent Hook to the Synchronization Audit Protocol.

v0.5 completes the first protocol arc by allowing AI agents to detect possible synchronization signals and prepare candidate records without producing final verdicts.

Added

  • Added docs/ai-agent-hook.md
  • Added schemas/ai-agent-hook.schema.json
  • Added examples/ai-agent-hook.example.yaml
  • Updated scripts/validate_examples.py to validate AI Agent Hook examples

AI Agent Hook

The AI Agent Hook provides a controlled entry point for automatic synchronization signal detection.

It allows an AI agent to detect possible similarity signals and prepare:

  • candidate synchronization audit records
  • candidate structure fingerprints
  • candidate case studies
  • candidate defense bridge packets
  • missing evidence lists
  • non-verdict summaries

Detection Signals

v0.5 supports detection of:

  • vocabulary overlap
  • structural sequence overlap
  • metaphor overlap
  • dependency pattern overlap
  • transformation pattern overlap
  • trace gaps
  • AI context dependency
  • mixed signals

Safety Boundary

AI may detect.

AI may prepare.

AI must not judge.

AI must not:

  • declare theft
  • assign legal responsibility
  • treat similarity as proof
  • treat candidate classification as final
  • bypass human review
  • publish accusation-level claims automatically

First Complete Protocol Arc

v0.5 completes the first protocol arc:

v0.1 = Audit Ruler
v0.2 = Case Study Layer
v0.3 = Structure Fingerprint / Trace Layer
v0.4 = Defense Court Protocol Integration
v0.5 = AI Agent Hook / Automatic Synchronization Detection
Principle
AI detects.
AI prepares.
AI does not judge.
Validation

GitHub Actions passed successfully.

All synchronization audit records, structure fingerprints, defense court bridges, and AI agent hook examples validate against their schemas.

Roadmap
v0.6 = Unified Trace Receipt / AI Search Trace Receipt integration
v0.7 = Multi-case Synchronization Graph
v0.8 = Confidence Calibration and Evidence Weighting
v0.9 = Human Review Workflow / Review Notes
v1.0 = Stable Synchronization Audit Standard
Closing Note

v0.5 gives the protocol an automated radar without giving it an automated trigger.

The agent may detect the shadow.

The human review boundary decides what the shadow means.

# Synchronization Audit Protocol v0.4.0-candidate — Defense Court Protocol Integration

This release adds Defense Court Protocol Integration to the Synchronization Audit Protocol.

v0.4 introduces a non-verdict bridge layer for preparing synchronization audit records, structure fingerprints, case studies, and trace references for human review.

## Added

- Added `docs/defense-court-integration.md`
- Added `schemas/defense-court-bridge.schema.json`
- Added `examples/defense-court-bridge.example.yaml`
- Updated `scripts/validate_examples.py` to validate Defense Court Bridge examples

## Defense Court Bridge

The Defense Court Bridge connects:

- Synchronization Audit Records
- Structure Fingerprints
- Case Studies
- Trace Layer references
- Evidence status
- Classification stability
- Human review boundaries

It does not produce verdicts.

It prepares review packets.

## Human Review Boundary

v0.4 makes explicit that AI may assist with:

- summarization
- structural comparison
- missing evidence review
- non-verdict packet preparation

AI must not:

- declare theft
- assign legal responsibility
- convert similarity into proof
- treat fingerprints as verdicts
- bypass human review

## Deferred Review Output

v0.4 supports:

```text
defer_until_trace_evidence_exists

This allows cases to remain unresolved when trace evidence is insufficient.

Principle
Audit is not accusation.
Fingerprint is not proof.
Bridge is not verdict.
Human review is the gate.
Validation

GitHub Actions passed successfully.

All synchronization audit records, structure fingerprints, and defense court bridge examples validate against their schemas.

Roadmap
v0.1 = Place the audit ruler
v0.2 = Add case studies
v0.3 = Connect to Structure Fingerprint / Trace Layer
v0.4 = Connect to Defense Court Protocol
v0.5 = Add AI Agent Hook / automatic synchronization detection
Closing Note

v0.4 gives the protocol a review boundary.

It keeps similarity from becoming accusation, and keeps audit records human-governed before any formal judgment.

# Synchronization Audit Protocol v0.3.0-candidate — Structure Fingerprint / Trace Layer

This release adds the Structure Fingerprint and Trace Layer Integration to the Synchronization Audit Protocol.

v0.1 placed the audit ruler.

v0.2 applied the ruler to multiple case studies.

v0.3 introduces a structured comparison layer for recording what is similar without treating similarity as proof.

## Added

- Added `schemas/structure-fingerprint.schema.json`
- Added `examples/structure-fingerprint.example.yaml`
- Added `docs/structure-fingerprint.md`
- Added `docs/trace-layer-integration.md`
- Updated `scripts/validate_examples.py` to validate multiple schema/example groups

## Structure Fingerprint Components

The new Structure Fingerprint schema supports:

- `vocabulary_signature`
- `structural_sequence`
- `metaphor_signature`
- `dependency_pattern`
- `transformation_pattern`
- `trace_layer_refs`

## Trace Layer Integration

v0.3 connects structural comparison to trace evidence anchors such as:

- synchronization audit records
- case studies
- trace receipts
- source contribution graphs
- repository commits
- public articles
- AI outputs
- external sources

Trace references are pointers.

They are not automatic proof.

## Principle

```text
A fingerprint is a map of resemblance.
It is not a verdict.
Purpose

v0.3 does not introduce automatic judgments.

It does not prove influence, imitation, copying, or causality.

It provides a comparison aid that helps Synchronization Audit Records evaluate structural similarity more carefully.

Validation

GitHub Actions passed successfully.

All synchronization audit record examples and the structure fingerprint example validate against their schemas.

Roadmap
v0.1 = Place the audit ruler
v0.2 = Add case studies
v0.3 = Connect to Structure Fingerprint / Trace Layer
v0.4 = Connect to Defense Court Protocol
v0.5 = Add AI Agent Hook / automatic synchronization detection
Closing Note

v0.3 gives the radar a contour detector.

It can now record not only that something appears similar, but what part of the structure appears similar.

# Synchronization Audit Protocol v0.2.0-candidate — Case Study Layer

This release adds the Case Study Layer to the Synchronization Audit Protocol.

v0.1 placed the audit ruler.

v0.2 applies that ruler to multiple concrete similarity cases while preserving conservative classification discipline.

## Added

- Added `docs/case-study-method.md`
- Added case study documentation:
  - `docs/case-studies/centralized-ai-backlash-memory-inflation.md`
  - `docs/case-studies/data-center-local-resistance.md`
  - `docs/case-studies/ai-search-trace-origin-opacity.md`
- Added multiple example audit records:
  - `sync-audit-record.centralized-ai-backlash-memory-inflation.example.yaml`
  - `sync-audit-record.data-center-local-resistance.example.yaml`
  - `sync-audit-record.ai-search-trace-origin-opacity.example.yaml`
- Updated `scripts/validate_examples.py` to validate all matching synchronization audit examples.

## Classification Coverage

v0.2 demonstrates conservative use of the A–E classification model:

```text
centralized_ai_backlash_memory_inflation = B / structural_convergence
data_center_local_resistance = B / structural_convergence
ai_search_trace_origin_opacity = E / ai_reconstruction
Purpose

The purpose of v0.2 is not to prove imitation, theft, influence, or causality.

The purpose is to demonstrate how observed similarities can be recorded, reviewed, and classified using a repeatable evidence matrix.

Evidence Discipline

The case studies preserve the distinction between:

similarity
convergence
indirect influence
direct reference
AI-side reconstruction

This prevents “similarity” from being collapsed into accusation.

Validation

GitHub Actions passed successfully.

All example audit records validate against sync-audit-record.schema.json.

Roadmap
v0.1 = Place the audit ruler
v0.2 = Add case studies
v0.3 = Connect to Structure Fingerprint / Trace Layer
v0.4 = Connect to Defense Court Protocol
v0.5 = Add AI Agent Hook / automatic synchronization detection
Closing Note

v0.2 turns the protocol from a ruler into a measuring practice.

The radar is no longer theoretical.

It has begun recording the sky.

# Synchronization Audit Protocol v0.1.0-candidate — Place the Audit Ruler

This is the first candidate release of the Synchronization Audit Protocol.

v0.1 establishes a minimal audit structure for classifying observed similarities without assuming influence, imitation, causality, or AI-side reconstruction.

## Core Principle

Similarity is not proof.

This protocol provides a conservative evidence framework for distinguishing:

- accidental synchronization
- structural convergence
- indirect influence
- direct reference or imitation
- AI-side reconstruction

## Added

- Initial repository structure
- `sync-audit-record.schema.json`
- `sync-audit-record.example.yaml`
- `scripts/validate_examples.py`
- GitHub Actions validation workflow
- `docs/synchronization-audit-protocol.md`
- README and CHANGELOG
- Fixed A–E classification model:
  - `A = accidental_sync`
  - `B = structural_convergence`
  - `C = indirect_influence`
  - `D = direct_reference_or_imitati...
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