wuyang
57 lines
1.6 KiB
JSON
57 lines
1.6 KiB
JSON
{
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"id": "agent-memory-pilot",
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"title": "Agent Memory: 从存储历史到编译经验",
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"status": "pilot",
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"version": 1,
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"created_at": "2026-07-10",
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"estimated_minutes": 45,
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"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？",
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"learning_outcomes": [
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"区分长上下文、检索记忆、执行状态和程序记忆",
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"根据任务选择合适的 memory 表示与控制策略",
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"设计 write、manage、read、learn、govern 闭环",
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"使用 budget-matched baseline 和分层错误指标评估 memory",
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"识别持久 memory 的遗漏、污染、过期和权限风险"
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],
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"artifacts": [
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{"type": "map", "path": "knowledge-map.md"},
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{"type": "chapter", "path": "chapter.md"},
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{"type": "evidence", "path": "evidence-matrix.md"},
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{"type": "recall", "path": "active-recall.md"}
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],
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"anchor_papers": [
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"2603.07670",
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"2605.18421",
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"2605.10870",
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"2603.15666",
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"2606.04315",
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"2606.06090",
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"2606.10677",
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"2606.11680",
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"2606.25161",
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"2605.08374",
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"2606.15017",
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"2605.14421"
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],
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"concepts": [
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"decision-sufficient-state",
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"write-manage-read-loop",
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"episodic-memory",
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"semantic-memory",
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"procedural-memory",
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"execution-state",
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"agentic-retrieval",
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"memory-consolidation",
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"credit-assignment",
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"provenance",
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"selective-forgetting",
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"budget-matched-evaluation"
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],
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"assessment": {
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"path": "active-recall.md",
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"question_count": 12,
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"max_score": 36,
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"review_intervals_days": [1, 3, 7]
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}
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}
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