From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models
arXiv:2606.03660v1 Announce Type: new 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. 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 str
The rapid deployment of LLMs into specialized scientific domains like chemistry necessitates more robust, scalable, and auditable evaluation methods that go beyond superficial correct answers.
This development addresses a critical limitation in AI evaluation, enabling more trustworthy and reliable AI assistants in sensitive fields, which is vital for industrial adoption and safety.
The introduction of rule-verifiable benchmarks like ChemCoTBench-V2 shifts LLM evaluation from costly human-centric methods to automated, auditable process-level analysis, improving diagnostic capabilities.
- · AI developers
- · Chemical research
- · Pharmaceutical industry
- · LLM evaluation platforms
- · LLMs with poor reasoning capabilities
- · Human-centric evaluation services
- · Legacy chemistry simulation software
Improved reliability and safety of AI-assisted chemical research and development.
Accelerated discovery of new materials, drugs, and chemical processes due to more trustworthy AI co-pilots.
Enhanced trust in AI for complex scientific problem-solving, potentially expanding AI application into other high-stakes domains.
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Read at arXiv cs.AI