agent/papers/items/2026-2606-01041-expweaver-llm-agents-learn-from-experience-via-latent-rag.md

Paper: ExpWeaver: LLM Agents Learn from Experience via Latent RAG

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type: paper title: "ExpWeaver: LLM Agents Learn from Experience via Latent RAG" authors: Tao Feng, Tianyang Luo, Jingjun Xu, Zhigang Hua, Yan Xie, Shuang Yang, Ge Liu, Jiaxuan You year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.01041 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-31 updated_at: 2026-05-31 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• planning
• rag
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.CL related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: planning-agent, rag-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: planning-agent, rag-agent
• inferred topics: agent-evaluation, coding-agent, planning, rag, reasoning, tool-use
• arXiv categories: cs.CL
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.01041