LLMs enhanced for chemical reaction mechanism reasoning with new benchmark

Researchers have developed a new method to enhance the chemical reasoning capabilities of large language models (LLMs) by focusing on mechanistic reasoning for chemical reactions. They created a large-scale dataset and established the FukuyamaBench benchmark, derived from Fukuyama's Advanced Organic Reaction Mechanism book, to evaluate LLMs on complex, step-by-step reaction pathways. A fine-tuned Qwen3-30B-A3B model achieved an 8.3% exact pathway match on FukuyamaBench Set A, outperforming the specialized FlowER model, which scored 5.1%. This demonstrates that training LLMs with mechanism-aware data significantly improves their chemical reasoning abilities. AI

IMPACT Enhances LLM capabilities in scientific reasoning, potentially improving applications in chemistry and drug discovery.

RANK_REASON The cluster contains an academic paper detailing a new method and benchmark for evaluating LLMs on a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• FlowER
• FukuyamaBench
• Fukuyama's Advanced Organic Reaction Mechanism
• Gotit.pub
• Hugging Face
• IArxiv
• Qwen3-30B-A3B
• ScienceCast

• paper
• model release

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