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    <abstract>Large language models (LLMs) often struggle with complex reasoning tasks due to their limitations in addressing the vast reasoning space and inherent ambiguities of natural language. We propose the Mixture-of-Search-Agents (MOSA) paradigm, a novel approach leveraging the collective expertise of multiple LLMs to enhance search-based reasoning. MOSA integrates diverse reasoning pathways by combining independent exploration with iterative refinement among LLMs, mitigating the limitations of single-model approaches. Using Monte Carlo Tree Search (MCTS) as a backbone, MOSA enables multiple agents to propose and aggregate reasoning steps, resulting in improved accuracy. Our comprehensive evaluation across four reasoning benchmarks demonstrates MOSA’s consistent performance improvements over single-agent and other multi-agent baselines, particularly in complex mathematical and commonsense reasoning tasks.</abstract>
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%0 Conference Proceedings
%T Multi-LLM Collaborative Search for Complex Problem Solving
%A Yang, Sen
%A Li, Yafu
%A Lam, Wai
%A Cheng, Yu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-multi
%X Large language models (LLMs) often struggle with complex reasoning tasks due to their limitations in addressing the vast reasoning space and inherent ambiguities of natural language. We propose the Mixture-of-Search-Agents (MOSA) paradigm, a novel approach leveraging the collective expertise of multiple LLMs to enhance search-based reasoning. MOSA integrates diverse reasoning pathways by combining independent exploration with iterative refinement among LLMs, mitigating the limitations of single-model approaches. Using Monte Carlo Tree Search (MCTS) as a backbone, MOSA enables multiple agents to propose and aggregate reasoning steps, resulting in improved accuracy. Our comprehensive evaluation across four reasoning benchmarks demonstrates MOSA’s consistent performance improvements over single-agent and other multi-agent baselines, particularly in complex mathematical and commonsense reasoning tasks.
%U https://aclanthology.org/2026.findings-acl.2115/
%P 42599-42614

Markdown (Informal)

[Multi-LLM Collaborative Search for Complex Problem Solving](https://aclanthology.org/2026.findings-acl.2115/) (Yang et al., Findings 2026)

• Multi-LLM Collaborative Search for Complex Problem Solving (Yang et al., Findings 2026)

ACL

• Sen Yang, Yafu Li, Wai Lam, and Yu Cheng. 2026. Multi-LLM Collaborative Search for Complex Problem Solving. In Findings of the Association for Computational Linguistics: ACL 2026, pages 42599–42614, San Diego, California, United States. Association for Computational Linguistics.