wuyang
70 lines
1.8 KiB
Markdown
70 lines
1.8 KiB
Markdown
# Paper: Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents
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---
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type: paper
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title: "Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents"
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authors: Hao-Lun Hsu, Nikki Lijing Kuang, Boyi Liu, Zhewei Yao, Yuxiong He
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year: 2026
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venue: arXiv
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url: https://arxiv.org/abs/2606.11680
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code_url:
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source: arxiv
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collected_at: 2026-07-08
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published_at: 2026-06-10
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updated_at: 2026-06-10
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status: skimmed
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relevance: high
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topics:
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- agent-evaluation
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- computer-use
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- embodied-agent
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- memory
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- planning
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- rag
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- reasoning
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methods:
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- hierarchical-memory-workspace
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- memory-skill-evolution
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- rl-retrieval-agent
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benchmarks:
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- ALFWorld
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- LoCoMo
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- LongMemEval
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models:
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-
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datasets:
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- cs.AI
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- cs.CL
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- cs.LG
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related_concepts:
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- memory-organization
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- agentic-retrieval
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related_jobs:
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-
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related_experiments:
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- KC-001-agent-memory-pilot
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related_projects:
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- learning/agent-memory
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collection_score: 18
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collection_queries: agent-memory
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---
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## One-line Takeaway
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Memory construction 和 retrieval 应分开优化：强模型负责长期组织，轻量策略负责沿层级 workspace 取回最小充分证据。
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## Pilot Skim
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- problem: 联合优化构建与检索会让稀疏任务 reward 无法归因，也混淆两个不同时间尺度的职责。
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- method: 高层 manager 组织带 provenance 的文件层级，retriever 用 Bash-like tools 导航并通过 RL 优化。
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- evidence: ALFWorld strict context 下达到 56.7/73.9 SR；小型 Qwen retriever 在对话数据训练后迁移到其他域。
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- boundary: weak manager 造成的组织缺陷无法由强 retriever 补回，系统依赖高能力 construction model。
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## Used In
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- [Agent Memory evidence matrix](../../learning/agent-memory/evidence-matrix.md)
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## Links
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- arXiv: https://arxiv.org/abs/2606.11680
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