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# Paper: Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems
---
type: paper
title: "Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems"
authors: Zhezheng Hao, Tianfu Wang, Huanshuo Dong, Ziyan Liu, Hong Wang, Xiankun Lin, Qiang Lin, Can Wang, et al.
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2605.29790
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-05-28
updated_at: 2026-05-28
status: queued
relevance: high
topics:
- agent-evaluation
- multi-agent
- planning
methods:
-
benchmarks:
-
models:
-
datasets:
- cs.MA
- cs.AI
related_concepts:
-
related_jobs:
-
related_experiments:
-
related_projects:
-
collection_score: 14
collection_queries: agent-evaluation
---
## One-line Takeaway
Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.
## Why Collected
- matched queries: agent-evaluation
- inferred topics: agent-evaluation, multi-agent, planning
- arXiv categories: cs.MA, cs.AI
- collection score: 14
## 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/2605.29790