# Paper: Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents

---
type: paper
title: "Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents"
authors: Yaoqi Chen, Haibin Lai, Yuru Feng, Chuyu Han, Qianxi Zhang, Baotong Lu, Menghao Li, Xinjiang Wang, et al.
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.06090
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-04
updated_at: 2026-06-04
status: skimmed
relevance: high
topics:
  - computer-use
  - memory
  - planning
  - rag
  - tool-use
methods:
  - hierarchical-execution-state-tree
  - grow-compress-maintain-revise
  - error-branch-isolation
benchmarks:
  - MemoryArena
models:
  -
datasets:
  - cs.AI
related_concepts:
  - execution-state
  - long-horizon-agent
related_jobs:
  -
related_experiments:
  - KC-001-agent-memory-pilot
related_projects:
  - learning/agent-memory
collection_score: 16
collection_queries: agent-memory, rag-agent
---

## One-line Takeaway

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。

## Pilot Skim

- 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。
- method: 两层 state tree 保存 action-observation 与 subgoal summary，通过 Grow、Compress、Maintain、Revise 管理当前路径。
- 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。
- boundary: 证据针对 interdependent long-horizon tasks，不直接代表事实问答或个性化 memory。

## Used In

- [Agent Memory evidence matrix](../../learning/agent-memory/evidence-matrix.md)

## Links

- arXiv: https://arxiv.org/abs/2606.06090