{
  "id": "agent-memory-pilot",
  "title": "Agent Memory: 从存储历史到编译经验",
  "status": "failed",
  "superseded_by": "research/memory/findings.md",
  "version": 1,
  "created_at": "2026-07-10",
  "estimated_minutes": 45,
  "core_question": "Agent 怎样在资源、可靠性和安全约束下，把历史压缩成能改善未来决策的状态？",
  "learning_outcomes": [
    "区分长上下文、检索记忆、执行状态和程序记忆",
    "根据任务选择合适的 memory 表示与控制策略",
    "设计 write、manage、read、learn、govern 闭环",
    "使用 budget-matched baseline 和分层错误指标评估 memory",
    "识别持久 memory 的遗漏、污染、过期和权限风险"
  ],
  "artifacts": [
    {"type": "map", "path": "knowledge-map.md"},
    {"type": "chapter", "path": "chapter.md"},
    {"type": "evidence", "path": "evidence-matrix.md"},
    {"type": "recall", "path": "active-recall.md"}
  ],
  "anchor_papers": [
    "2603.07670",
    "2605.18421",
    "2605.10870",
    "2603.15666",
    "2606.04315",
    "2606.06090",
    "2606.10677",
    "2606.11680",
    "2606.25161",
    "2605.08374",
    "2606.15017",
    "2605.14421"
  ],
  "concepts": [
    "decision-sufficient-state",
    "write-manage-read-loop",
    "episodic-memory",
    "semantic-memory",
    "procedural-memory",
    "execution-state",
    "agentic-retrieval",
    "memory-consolidation",
    "credit-assignment",
    "provenance",
    "selective-forgetting",
    "budget-matched-evaluation"
  ],
  "assessment": {
    "path": "active-recall.md",
    "question_count": 12,
    "max_score": 36,
    "review_intervals_days": [1, 3, 7]
  }
}