[2509.23768] From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning

Computer Science > Artificial Intelligence arXiv:2509.23768 (cs) [Submitted on 28 Sep 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning Authors:Cheng Yang, Jiaxuan Lu, Haiyuan Wan, Junchi Yu, Feiwei Qin View a PDF of the paper titled From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning, by Cheng Yang and 4 other authors View PDF HTML (experimental) Abstract: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 outpe...