LabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making

Abstract:Laboratory experiments involve more than following predefined protocols; they require adaptive decision-making as the experimental state changes. Existing scientific benchmarks rarely assess such evidence-based decisions in laboratory settings. Here we present LabOPBench, a multimodal benchmark of 756 expert-authored, independently reviewed questions in organic synthesis and materials science. Each item pairs laboratory evidence with procedural context and an open-ended judgment, evaluated using a question-specific scoring rubric. Eight multimodal models obtain overall normalized scores of 28.8-45.8%, leaving substantial room for improvement. On a controlled 100-question subset, the strongest models outperform undergraduate participants on average but remain below the average performance of PhD participants. Expert analysis of responses from the three leading models identifies Evidence Misreading and Evidence-Decision Conflict as the dominant error patterns. These results reveal that current models still struggle to turn multimodal perception into reliable laboratory decision-making.

Artificial Intelligence (cs.AI)

arXiv:2610.19780 [cs.AI]

(or arXiv:2610.19780v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.19780 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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