wuyang
64 lines
1.8 KiB
Markdown
64 lines
1.8 KiB
Markdown
# Paper: Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents
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---
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type: paper
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title: "Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents"
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authors: Yaoqi Chen, Haibin Lai, Yuru Feng, Chuyu Han, Qianxi Zhang, Baotong Lu, Menghao Li, Xinjiang Wang, et al.
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year: 2026
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venue: arXiv
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url: https://arxiv.org/abs/2606.06090
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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: 2026-06-04
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updated_at: 2026-06-04
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status: skimmed
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relevance: high
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topics:
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- computer-use
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- memory
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- planning
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- rag
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- tool-use
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methods:
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- hierarchical-execution-state-tree
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- grow-compress-maintain-revise
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- error-branch-isolation
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benchmarks:
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- MemoryArena
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models:
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-
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datasets:
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- cs.AI
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related_concepts:
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- execution-state
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- long-horizon-agent
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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: 16
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collection_queries: agent-memory, rag-agent
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---
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## One-line Takeaway
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长程任务需要维护 root-to-current 执行状态并隔离错误分支，而不是从相似记录中临时拼装上下文。
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## Pilot Skim
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- problem: 相似度组织会割裂执行依赖，并把有效和错误轨迹重新混入同一上下文。
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- method: 两层 state tree 保存 action-observation 与 subgoal summary，通过 Grow、Compress、Maintain、Revise 管理当前路径。
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- evidence: MemoryArena 上相对 baselines 平均提高 7.8 至 20.4pp，并比长上下文减少 55.1% token。
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- boundary: 证据针对 interdependent long-horizon tasks，不直接代表事实问答或个性化 memory。
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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/2606.06090
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