@inproceedings{wang-etal-2026-metagen,
title = "{M}eta{G}en: Self-Evolving Roles and Topologies for Multi-Agent {LLM} Reasoning",
author = "Wang, Yimeng and
Zhao, Jiaxing and
Xie, Hongbin and
Ma, Hexing and
Lei, Yuzhen and
Liu, Shuangxue and
Song, Xuan and
Zhang, Zichen and
Zhang, Haoran",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2005/",
pages = "42721--42734",
ISBN = "979-8-89176-462-0",
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 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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<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 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.</abstract>
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%0 Conference Proceedings
%T MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning
%A Wang, Yimeng
%A Zhao, Jiaxing
%A Xie, Hongbin
%A Ma, Hexing
%A Lei, Yuzhen
%A Liu, Shuangxue
%A Song, Xuan
%A Zhang, Zichen
%A Zhang, Haoran
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-metagen
%X 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 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.
%U https://aclanthology.org/2026.findings-emnlp.2005/
%P 42721-42734
Markdown (Informal)
[MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning](https://aclanthology.org/2026.findings-emnlp.2005/) (Wang et al., Findings 2026)
ACL
- Yimeng Wang, Jiaxing Zhao, Hongbin Xie, Hexing Ma, Yuzhen Lei, Shuangxue Liu, Xuan Song, Zichen Zhang, and Haoran Zhang. 2026. MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 42721–42734, Budapest, Hungary. Association for Computational Linguistics.