Recent advancements have increasingly focused on leveraging large language models (LLMs) to construct autonomous agents for complex problem-solving tasks. However, existing approaches predominantly employ a single-agent framework to generate search branches and estimate rewards during Monte Carlo Tree Search (MCTS) planning. This single-agent paradigm inherently limits exploration capabilities, often resulting in insufficient diversity among generated branches and suboptimal planning performance. To overcome these limitations, we propose Synergistic Multi-agent Planning with Heterogeneous langauge model assembly (SYMPHONY), a novel multi-agent planning framework that integrates a pool of heterogeneous language model-based agents. By leveraging diverse reasoning patterns across agents, SYMPHONY enhances rollout diversity and facilitates more effective exploration. Empirical results across multiple benchmark tasks show that SYMPHONY achieves strong performance even when instantiated with open-source LLMs deployable on consumer-grade hardware. When enhanced with cloud-based LLMs accessible via API, SYMPHONY demonstrates further improvements, outperforming existing state-of-the-art baselines and underscoring the effectiveness of heterogeneous multi-agent coordination in planning tasks.

翻译：近年来，研究重点日益聚焦于利用大语言模型（LLM）构建自主智能体以完成复杂问题求解任务。然而，现有方法主要采用单智能体框架，在蒙特卡洛树搜索（MCTS）规划过程中生成搜索分支并估计奖励。这种单智能体范式本质上限制了探索能力，通常导致生成的分支多样性不足以及规划性能欠佳。为克服这些限制，我们提出了基于异构语言模型组合的协同多智能体规划（SYMPHONY），这是一个新颖的多智能体规划框架，它整合了一组基于异构语言模型的智能体。通过利用不同智能体间多样化的推理模式，SYMPHONY增强了模拟推演的多样性，并促进了更有效的探索。在多个基准任务上的实证结果表明，即使使用可在消费级硬件上部署的开源LLM进行实例化，SYMPHONY也能实现强劲的性能。当通过API接入基于云的LLM进行增强时，SYMPHONY展现出进一步的性能提升，超越了现有的先进基线方法，并凸显了异构多智能体协调在规划任务中的有效性。