从“是什么”到“为什么”：基于证据的化学反应条件推理多智能体系统

摘要

化学反应推荐旨在为化学反应选择适宜的反应条件参数，这对加速化学科学发展至关重要。随着大语言模型（LLMs）的快速发展，人们越来越关注利用其推理与规划能力进行反应条件推荐。尽管现有方法已取得一定成功，但它们很少解释推荐反应条件背后的逻辑，这限制了其在高风险科学工作流程中的应用。在本研究中，我们提出了ChemMAS，一个多智能体系统，它将条件预测重构为基于证据的推理任务。ChemMAS将任务分解为机制基础、多通道回忆、约束感知的智能体辩论及理由整合四个环节。每个决策均以化学知识和检索到的先例为基础，提供可解释的论证。实验表明，ChemMAS在领域特定基线模型上实现了20-35%的性能提升，并在Top-1准确率上超越通用LLMs 10-15%，同时提供了可验证、可信赖的人类可理解理由，为科学发现中的可解释AI树立了新的范式。

English

The chemical reaction recommendation is to select proper reaction condition parameters for chemical reactions, which is pivotal to accelerating chemical science. With the rapid development of large language models (LLMs), there is growing interest in leveraging their reasoning and planning capabilities for reaction condition recommendation. Despite their success, existing methods rarely explain the rationale behind the recommended reaction conditions, limiting their utility in high-stakes scientific workflows. In this work, we propose ChemMAS, a multi-agent system that reframes condition prediction as an evidence-based reasoning task. ChemMAS decomposes the task into mechanistic grounding, multi-channel recall, constraint-aware agentic debate, and rationale aggregation. Each decision is backed by interpretable justifications grounded in chemical knowledge and retrieved precedents. Experiments show that ChemMAS achieves 20-35% gains over domain-specific baselines and outperforms general-purpose LLMs by 10-15% in Top-1 accuracy, while offering falsifiable, human-trustable rationales, which establishes a new paradigm for explainable AI in scientific discovery.

从“是什么”到“为什么”：基于证据的化学反应条件推理多智能体系统

From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning

摘要

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