Multi-LLM Collaborative Search for Complex Problem Solving

• URL: http://arxiv.org/abs/2502.18873v1
• Date: Wed, 26 Feb 2025 06:31:04 GMT
• Title: Multi-LLM Collaborative Search for Complex Problem Solving
• Authors: Sen Yang, Yafu Li, Wai Lam, Yu Cheng,
• Abstract summary: We propose the Mixture-of-Search-Agents (MoSA) paradigm to enhance search-based reasoning.<n>MoSA integrates diverse reasoning pathways by combining independent exploration with iterative refinement among LLMs.<n>Using Monte Carlo Tree Search (MCTS) as a backbone, MoSA enables multiple agents to propose and aggregate reasoning steps, resulting in improved accuracy.
• Score: 54.194370845153784
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Guiding Reasoning in Small Language Models with LLM Assistance [23.3038074903744]
  Small Language Models cast doubt suitability for tasks demanding deep, multi-step logical deduction. This paper introduces a framework called Small Reasons, Large Hints, which selectively augments SLM reasoning with targeted guidance from large language models. Our experiments on mathematical reasoning datasets demonstrate that targeted external scaffolding significantly improves performance.
  arXiv  Detail & Related papers  (2025-04-14T06:32:45Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [54.787341008881036]
  We introduce Reinforced Meta-thinking Agents (ReMA), a novel framework that leverages Multi-Agent Reinforcement Learning (MARL) to elicit meta-thinking behaviors. ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions. Experimental results demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark [73.27104042215207]
  We introduce EMMA, a benchmark targeting organic multimodal reasoning across mathematics, physics, chemistry, and coding.<n>EMMA tasks demand advanced cross-modal reasoning that cannot be addressed by reasoning independently in each modality.<n>Our evaluation of state-of-the-art MLLMs on EMMA reveals significant limitations in handling complex multimodal and multi-step reasoning tasks.
  arXiv  Detail & Related papers  (2025-01-09T18:55:52Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM [15.687878949848182]
  Large language models (LLMs) have shown limitations in tasks requiring complex logical reasoning and multi-step problem-solving.<n>We introduce MTMT (Multi-thinking Modes Tree), a novel method that interacts with LLMs to construct a thought tree.<n>We evaluate the performance of MTMT under different parameter configurations, using GPT-4o mini as the base model.
  arXiv  Detail & Related papers  (2024-12-05T09:05:30Z)
• Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models [1.4582633500696451]
  Large Language Models (LLMs) still face challenges when dealing with complex reasoning tasks. This paper proposes a new method that integrates different LLMs to expand the knowledge boundary. Experiments on arithmetic datasets have validated the effectiveness of the method.
  arXiv  Detail & Related papers  (2024-11-25T08:42:33Z)
• Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models [84.15513004135576]
  Current research enhances the reasoning performance of Large Language Models (LLMs) by sampling multiple reasoning chains and ensembling based on the answer frequency. This approach fails in scenarios where the correct answers are in the minority. We introduce a hierarchical reasoning aggregation framework AoR, which selects answers based on the evaluation of reasoning chains.
  arXiv  Detail & Related papers  (2024-05-21T17:12:19Z)
• Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning [14.635361844362794]
  Smurfs' is a cutting-edge multi-agent framework designed to revolutionize the application of large language models. Smurfs can enhance the model's ability to solve complex tasks at no additional cost.
  arXiv  Detail & Related papers  (2024-05-09T17:49:04Z)
• Large Multimodal Agents: A Survey [78.81459893884737]
  Large language models (LLMs) have achieved superior performance in powering text-based AI agents. There is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain. This review aims to provide valuable insights and guidelines for future research in this rapidly evolving field.
  arXiv  Detail & Related papers  (2024-02-23T06:04:23Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.