From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models
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. Existing process-level evaluators are hard to scale because LLM judges and human step-level process annotation are costly, inconsistent, and vulnerable to hallucination. We introduce ChemCoTBench-V2, a rule-verifiable diagnostic benchmark for low-cost, auditable evaluation of structured, verifier-addressable chemical reasoning traces. It spans molecular understanding, molecule editing, molecular optimization, and reaction...
| Authors | Hongyu Guo, Hao Li, He Cao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 June 2026 |
| Last updated at source | 3 June 2026 |
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