TMCS：面向组合化学问题求解的工具接地多智能体推理

中文摘要 AI 辅助

尽管大型语言模型（LLMs）在计算化学领域展现出潜力，但严格的组合化学问题仍然难以解决，因为它们需要定量约束的分子修饰、候选验证以及失败尝试后的系统性修订。现有的工具增强型化学智能体展示了有用的规划与工具使用能力，但很少提供用于性质驱动的分子优化和工作流级组合的统一循环。为弥合这一差距，我们提出了工具接地多智能体推理用于组合化学问题求解（TMCS），这是一个逐步的多智能体框架，将化学问题求解形式化为可解释的、工具增强的工作流。在任务层面，专门化的智能体利用外部工具、少样本轨迹记忆和结构化反思来迭代优化解决方案。在工作流层面，TMCS将生成、理解、编辑、描述和优化串联成一个闭环流水线。在多个化学任务上的评估表明，TMCS在开源和闭源基础模型上均持续增强化学推理能力，达到最先进的性能。

英文摘要

Despite the promise of Large Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because they require quantitatively constrained molecular modification, candidate validation, and systematic revision after failed attempts. Existing tool-augmented chemical agents demonstrate useful planning and tool use, but they rarely provide a unified loop for property-driven molecular optimization and workflow-level composition. To bridge this gap, we propose Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving (TMCS), a step-by-step multi-agent framework that formalizes chemical problem solving as an interpretable, tool-augmented workflow. At the task level, specialized agents leverage external tools, few-shot trajectory memory, and structured reflection to iteratively refine solutions. At the workflow level, TMCS chains generation, understanding, editing, description, and optimization into a closed-loop pipeline. Evaluations across multiple chemical tasks demonstrate that TMCS consistently enhances chemical reasoning across both open- and closed-source base models, achieving state-of-the-art performance.

发表机构

• East China Normal University（华东师范大学）
• Shanghai Innovation Institute（上海创新研究院）
• University College London（伦敦大学学院）

机构由 AI 辅助整理，请以论文原文为准。