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From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models

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From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models
#artificial intelligence#chemistry#machine learning
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The paper introduces ChemCoTBench-V2, a benchmark designed for evaluating chemical reasoning in large language models. It emphasizes the importance of process-level evaluation rather than just final answers, highlighting the potential for models to provide correct outputs while lacking sound reasoning. The benchmark aims to facilitate fine-grained comparisons between models and identify specific failures in their reasoning processes.

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Original publisherarXiv cs.AI
Canonical URLhttps://arxiv.org/abs/2606.03660
Publication timeWed, 03 Jun 2026 00:00:00 -0400
Retrieval time2026-06-03T04:11:55.408Z
Last seen2026-06-03T04:11:55.408Z
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Computer Science > Artificial Intelligence arXiv:2606.03660 (cs) [Submitted on 2 Jun 2026] Title:From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models Authors:Hongyu Guo, Hao Li, He Cao, Gongbo Zhang, Li Yuan View a PDF of the paper titled From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models, by Hongyu Guo and 4 other authors View PDF HTML (experimental) Abstract:Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers. This masks a critical failure mode: a model may output the correct molecule, product, or option while its reasoning violates chemical logic.

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