From What to Why: A Multi-Agent System for Evidence-based Chemical
  Reaction Condition Reasoning
Authors: name: Cheng Yang, name: Jiaxuan Lu, name: Haiyuan Wan, name: Junchi Yu, name: Feiwei Qin

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.