Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.19290 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918308036476928 |
|---|---|
| author | Wang, Yimeng Zhao, Jiaxing Xie, Hongbin Ma, Hexing Lei, Yuzhen Liu, Shuangxue Song, Xuan Zhang, Zichen Zhang, Haoran |
| author_facet | Wang, Yimeng Zhao, Jiaxing Xie, Hongbin Ma, Hexing Lei, Yuzhen Liu, Shuangxue Song, Xuan Zhang, Zichen Zhang, Haoran |
| contents | Large language models are increasingly deployed as multi-agent systems, where specialized roles communicate and collaborate through structured interactions to solve complex tasks that often exceed the capacity of a single agent. However, most existing systems still rely on a fixed role library and an execution-frozen interaction topology, a rigid design choice that frequently leads to task mismatch, prevents timely adaptation when new evidence emerges during reasoning, and further inflates inference cost. We introduce MetaGen, a training-free framework that adapts both the role space and the collaboration topology at inference time, without updating base model weights. MetaGen generates and rewrites query-conditioned role specifications to maintain a controllable dynamic role pool, then instantiates a constrained execution graph around a minimal backbone. During execution, it iteratively updates role prompts and adjusts structural decisions using lightweight feedback signals. Experiments on code generation and multi-step reasoning benchmarks show that MetaGen improves the accuracy and cost tradeoff over strong multi-agent baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19290 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning Wang, Yimeng Zhao, Jiaxing Xie, Hongbin Ma, Hexing Lei, Yuzhen Liu, Shuangxue Song, Xuan Zhang, Zichen Zhang, Haoran Computation and Language Large language models are increasingly deployed as multi-agent systems, where specialized roles communicate and collaborate through structured interactions to solve complex tasks that often exceed the capacity of a single agent. However, most existing systems still rely on a fixed role library and an execution-frozen interaction topology, a rigid design choice that frequently leads to task mismatch, prevents timely adaptation when new evidence emerges during reasoning, and further inflates inference cost. We introduce MetaGen, a training-free framework that adapts both the role space and the collaboration topology at inference time, without updating base model weights. MetaGen generates and rewrites query-conditioned role specifications to maintain a controllable dynamic role pool, then instantiates a constrained execution graph around a minimal backbone. During execution, it iteratively updates role prompts and adjusts structural decisions using lightweight feedback signals. Experiments on code generation and multi-step reasoning benchmarks show that MetaGen improves the accuracy and cost tradeoff over strong multi-agent baselines. |
| title | MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.19290 |