# Paper: Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents

---
type: paper
title: "Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents"
authors: Hao-Lun Hsu, Nikki Lijing Kuang, Boyi Liu, Zhewei Yao, Yuxiong He
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.11680
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-10
updated_at: 2026-06-10
status: skimmed
relevance: high
topics:
  - agent-evaluation
  - computer-use
  - embodied-agent
  - memory
  - planning
  - rag
  - reasoning
methods:
  - hierarchical-memory-workspace
  - memory-skill-evolution
  - rl-retrieval-agent
benchmarks:
  - ALFWorld
  - LoCoMo
  - LongMemEval
models:
  -
datasets:
  - cs.AI
  - cs.CL
  - cs.LG
related_concepts:
  - memory-organization
  - agentic-retrieval
related_jobs:
  -
related_experiments:
  - KC-001-agent-memory-pilot
related_projects:
  - learning/agent-memory
collection_score: 18
collection_queries: agent-memory
---

## One-line Takeaway

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。

## Pilot Skim

- 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。
- 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。
- 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。
- 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。

## Used In

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

## Links

- arXiv: https://arxiv.org/abs/2606.11680