Files
agent/papers/items/2026-2606-01041-expweaver-llm-agents-learn-from-experience-via-latent-rag.md
T
2026-07-08 12:25:30 +08:00

64 lines
1.4 KiB
Markdown

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