让化学反应条件推荐有理有据，可解释且可信。
From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning
- 用多智能体系统分解推理过程，每步都有化学知识支持。
- 相比基线模型，准确率提升20%-35%，超越通用大模型10%-15%。
- 适合需要可解释性与高可信度的科研人员使用。
化学反应条件推荐旨在为化学反应选择合适的条件参数，对加速化学科学发展至关重要。随着大语言模型（LLMs）的发展，人们开始利用其推理与规划能力进行反应条件推荐。然而，现有方法很少解释推荐背后的逻辑，限制了其在高风险科学工作流中的应用。本文提出ChemMAS，一个将条件预测重构为基于证据的推理任务的多智能体系统。该系统将任务分解为机理溯源、多通道召回、约束感知的智能体辩论和理由聚合四个阶段，每个决策均有可解释的依据，基于化学知识和检索到的先例。实验表明，ChemMAS在顶1准确率上比领域特定基线模型高出20%-35%，比通用大模型提升10%-15%，同时提供可验证、人类可信任的推理过程，为可解释人工智能在科学发现中的应用建立了新范式。
原文摘要 · Abstract (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.
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