Year: 2026
Type: conference-paper
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 multiagent baselines, particularly in complex mathematical and commonsense reasoning tasks.
Authors: Sen Yang, Yafu Li, Wai Lam, Yu Cheng
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416043580