MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning

• URL: http://arxiv.org/abs/2601.19290v1
• Date: Tue, 27 Jan 2026 07:24:35 GMT
• Title: MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning
• Authors: Yimeng Wang, Jiaxing Zhao, Hongbin Xie, Hexing Ma, Yuzhen Lei, Shuangxue Liu, Xuan Song, Zichen Zhang, Haoran Zhang,
• Abstract summary: We introduce MetaGen, a training-free framework that adapts both the role space and the collaboration topology at inference time.<n>We show that MetaGen improves the accuracy and cost tradeoff over strong multi-agent baselines.
• Score: 11.023742160114763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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