| name | memory-governance |
| description | 记忆治理 — 管理分层记忆系统的 Promote/衰减/去重/凝练，确保记忆质量与一致性 |
| version | 1.0.0 |
| license | MIT |
| metadata | {"author":"blackplume233","actant-tags":"memory,governance,knowledge-management,curator","layer":"kernel","agent":"actant-curator"} |
Memory Governance
Overview
Manage the layered memory system: Promote, decay, deduplication, and condensation. Ensure memory quality and consistency across Instance, Template, and Actant layers.
Instance Memory Promote
Purpose: Elevate high-value Instance memories to Template or Actant layers for broader reuse.
Promote Workflow
- Candidate review: Identify memories with high confidence, repeated use, or cross-session relevance
- Conflict resolution: Before Promote, check for contradictions with existing Template/Actant memories
- Promotion: Move approved candidates to
ac://memory/template/{templateId}/ or ac://memory/actant/learnings/
- Source attribution: Preserve origin Instance ID for traceability
Conflict Handling
- If Instance memory contradicts Template memory: flag for human review or apply recency/confidence rule
- If multiple Instances have conflicting memories: arbitrate by confidence score, usage count, or manual merge
Memory Deduplication
Purpose: Reduce redundancy and maintain a single source of truth.
- contentHash detection: Compute hash of normalized content; detect duplicates across layers
- Merge strategy: Keep the most comprehensive version; merge metadata (confidence, timestamps, sources)
- Refinement: Produce a condensed version that preserves all unique information
Memory Decay (Expiration)
Purpose: Archive or retire low-confidence or stale memories.
- Confidence decay rules: Apply time-based or usage-based decay to confidence scores
- Archive threshold: When confidence < threshold, move to archive (read-only, excluded from retrieval)
- Eviction: When archive exceeds capacity, delete lowest-value entries
- Audit trail: Log all decay/archive/eviction actions for debugging
Memory Conflict Arbitration
Purpose: Resolve contradictions when multiple Instances have conflicting memories.
- Detection: Compare memories on same topic/entity; flag semantic contradictions
- Arbitration strategies:
- Recency: Prefer newer memory
- Confidence: Prefer higher confidence
- Source authority: Prefer Template > Instance
- Manual: Escalate to human when automated resolution unclear
- Merge: When possible, merge into a nuanced single memory ("Usually X, but in context Y, Z")
Cross-Template Generic Learnings
Purpose: Identify and elevate learnings that apply across multiple Templates.
- Recognition: Detect patterns that recur across different Template contexts
- Promotion target:
ac://memory/actant/learnings/
- Use case: System-wide best practices, shared domain knowledge, common pitfalls
- Governance: Curator reviews before Promote; avoid Template-specific details in Actant learnings
