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BioProBench addresses a critical bottleneck in autonomous science by providing the first large-scale benchmark specifically designed to test LLMs on strict biological procedural logic and safety. The study reveals that while current models understand general biology well, they fail significantly at tasks requiring deep reasoning and quantitative precision essential for real-world experimentation. This resource offers both a training corpus and rigorous evaluation metrics needed to advance reliable AI agents in laboratory settings.
BioProBench: A Corpus and Benchmark for Biological Protocol Reasoning in Autonomous Science
arXiv:2505.07889v4 Announce Type: replace Abstract: The realization of autonomous scientific experimentation is currently limited by LLMs' struggle to grasp the strict procedural logic and accuracy required by biological protocols. To address this fundamental challenge, we present \textbf{BioProBench}, a comprehensive resource for procedural reasoning in biology. BioProBench is grounded in \textbf{BioProCorpus}, a foundational collection of 22,413 human-written protocols. From this corpus, we systematically constructed a dataset of 523,784 task instances, offering both a large-scale training resource and a rigorous benchmark with novel metrics. Evaluating 10 mainstream LLMs, we find that while general comprehension is high, performance drops significantly on tasks demanding deep reasoning, quantitative precision, and safety awareness. To demonstrate the value of BioProCorpus in mitigating these issues, we developed \textbf{ProAgent}, grounded in our corpus, ProAgent substantially advances the state-of-the-art. https://github.com/YuyangSunshine/bioprobench and https://huggingface.co/BioProBench.
Abstract:The realization of autonomous scientific experimentation is currently limited by LLMs' struggle to grasp the strict procedural logic and accuracy required by biological protocols. To address this fundamental challenge, we present \textbf{BioProBench}, a comprehensive resource for procedural reasoning in biology. BioProBench is grounded in \textbf{BioProCorpus}, a foundational collection of 22,413 human-written protocols. From this corpus, we systematically constructed a dataset of 523,784 task instances, offering both a large-scale training resource and a rigorous benchmark with novel metrics. Evaluating 10 mainstream LLMs, we find that while general comprehension is high, performance drops significantly on tasks demanding deep reasoning, quantitative precision, and safety awareness. To demonstrate the value of BioProCorpus in mitigating these issues, we developed \textbf{ProAgent}, grounded in our corpus, ProAgent substantially advances the state-of-the-art. this https URL and this https URL.
Computation and Language (cs.CL)
arXiv:2505.07889 [cs.CL]
(or arXiv:2505.07889v4 [cs.CL] for this version)
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