BenchBench-Cloning: Evaluating Molecular Cloning Design and Protocol Development

Authors: Aditya Sivakumar , Peter Ciaccia , Hassaan Qadir , Ian Hsu , Ashu Singhal

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Abstract: Molecular cloning is one of the most routine wet-lab workflows, yet existing evaluations of language models in biology mostly test knowledge recall or isolated steps rather than end-to-end experimental planning. We introduce BenchBench-Cloning, a benchmark for large language models comprising 65 molecular cloning tasks grounded in sequence records, laboratory notebooks, and published experimental studies. Planning a molecular cloning experiment requires defining a set of constructs needed for a scientific objective and a construction route to produce them. BenchBench-Cloning contains 50 protocol-development tasks, in which agents produce an executable protocol for a specified construct, and 15 construct-design tasks, in which agents determine the constructs a scientific objective calls for. Protocol-development tasks are evaluated by execution in a cloning simulator; construct-design tasks are evaluated with task-specific deterministic checks and, where needed, rubric-based LLM-as-a-judge evaluation. Across eight systems with repeated attempts per task, GPT-6 Astra scores highest at 30.6%, with the others between 8.6% and 22.2%, and the benchmark remains unsaturated under best-of-ten sampling. As integrating individual cloning decisions into complete, valid plans remains challenging even for frontier models, we expect BenchBench-Cloning to serve as a useful benchmark for tracking progress in experimental planning.

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Artificial Intelligence (cs.AI)

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arXiv:2610.33355 [cs.AI]

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arXiv:2610.33355v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33355

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arXiv-issued DOI via DataCite (pending registration)

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From: Aditya Sivakumar [ view email ]

[v1]

Fri, 23 Oct 2026 22:51:29 UTC (208 KB)

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