@inproceedings{cheng-etal-2026-optimas,
title = "{O}pti{MAS}: Automatically Optimize Multi-Agent System",
author = "Cheng, Yuxin and
Liu, Chang and
Yu, Hanxin and
Tan, Haochen and
Wu, Taiqiang and
Jin, Weiqiang and
Ran, Jie and
Wang, Kaibo and
Li, Xiaoguang and
Bai, Haoli and
Chesi, Graziano and
Wong, Ngai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.470/",
pages = "10794--10821",
ISBN = "979-8-89176-459-0",
abstract = "Automated evolution of Multi-Agent Systems (MAS) holds significant potential for reducing the manual effort required to design and optimize LLM-based agent architectures. However, extant search-based paradigms face a fundamental trade-off, where an expanded optimization scope exacerbates evolutionary instability, while discrete branch-and-discard search isolates insights across lineages. To address these limitations, we propose a continuous, data-driven optimization paradigm built upon a unified ReAct-based infrastructure that reconciles a broad optimization scope with operational stability. Under this paradigm, we present OptiMAS, a task-agnostic agentic optimizer that leverages textual interaction trajectories and task feedback as loss signals for end-to-end MAS evolution. Equipped with a novel dual-track memory mechanism, OptiMAS sustains performance improvement over extended optimization horizons. Evaluation on four heterogeneous agentic benchmarks with three varying scale and accessibility LLM backbones, demonstrates that OptiMAS consistently achieves competitive or superior accuracy relative to both domain-specialized hand-crafted systems and existing evolutionary methods. Our work establishes a practical milestone toward robust, automated MAS evolution."
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<abstract>Automated evolution of Multi-Agent Systems (MAS) holds significant potential for reducing the manual effort required to design and optimize LLM-based agent architectures. However, extant search-based paradigms face a fundamental trade-off, where an expanded optimization scope exacerbates evolutionary instability, while discrete branch-and-discard search isolates insights across lineages. To address these limitations, we propose a continuous, data-driven optimization paradigm built upon a unified ReAct-based infrastructure that reconciles a broad optimization scope with operational stability. Under this paradigm, we present OptiMAS, a task-agnostic agentic optimizer that leverages textual interaction trajectories and task feedback as loss signals for end-to-end MAS evolution. Equipped with a novel dual-track memory mechanism, OptiMAS sustains performance improvement over extended optimization horizons. Evaluation on four heterogeneous agentic benchmarks with three varying scale and accessibility LLM backbones, demonstrates that OptiMAS consistently achieves competitive or superior accuracy relative to both domain-specialized hand-crafted systems and existing evolutionary methods. Our work establishes a practical milestone toward robust, automated MAS evolution.</abstract>
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%0 Conference Proceedings
%T OptiMAS: Automatically Optimize Multi-Agent System
%A Cheng, Yuxin
%A Liu, Chang
%A Yu, Hanxin
%A Tan, Haochen
%A Wu, Taiqiang
%A Jin, Weiqiang
%A Ran, Jie
%A Wang, Kaibo
%A Li, Xiaoguang
%A Bai, Haoli
%A Chesi, Graziano
%A Wong, Ngai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F cheng-etal-2026-optimas
%X Automated evolution of Multi-Agent Systems (MAS) holds significant potential for reducing the manual effort required to design and optimize LLM-based agent architectures. However, extant search-based paradigms face a fundamental trade-off, where an expanded optimization scope exacerbates evolutionary instability, while discrete branch-and-discard search isolates insights across lineages. To address these limitations, we propose a continuous, data-driven optimization paradigm built upon a unified ReAct-based infrastructure that reconciles a broad optimization scope with operational stability. Under this paradigm, we present OptiMAS, a task-agnostic agentic optimizer that leverages textual interaction trajectories and task feedback as loss signals for end-to-end MAS evolution. Equipped with a novel dual-track memory mechanism, OptiMAS sustains performance improvement over extended optimization horizons. Evaluation on four heterogeneous agentic benchmarks with three varying scale and accessibility LLM backbones, demonstrates that OptiMAS consistently achieves competitive or superior accuracy relative to both domain-specialized hand-crafted systems and existing evolutionary methods. Our work establishes a practical milestone toward robust, automated MAS evolution.
%U https://aclanthology.org/2026.emnlp-main.470/
%P 10794-10821
Markdown (Informal)
[OptiMAS: Automatically Optimize Multi-Agent System](https://aclanthology.org/2026.emnlp-main.470/) (Cheng et al., EMNLP 2026)
ACL
- Yuxin Cheng, Chang Liu, Hanxin Yu, Haochen Tan, Taiqiang Wu, Weiqiang Jin, Jie Ran, Kaibo Wang, Xiaoguang Li, Haoli Bai, Graziano Chesi, and Ngai Wong. 2026. OptiMAS: Automatically Optimize Multi-Agent System. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 10794–10821, Budapest, Hungary. Association for Computational Linguistics.