Add Agent Memory learning pilot

2026-07-10 18:01:44 +08:00
parent 15b5cec933
commit 36c631e026
22 changed files with 1195 additions and 224 deletions
@@ -0,0 +1,56 @@
+{
+  "id": "agent-memory-pilot",
+  "title": "Agent Memory: 从存储历史到编译经验",
+  "status": "pilot",
+  "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]
+  }
+}