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arXiv Machine Learning By Marilyn Zhang, Tianfeng Chen, Mark E. Whiting

Biological Context Does Not Reliably Improve LLM-Guided Experimental Optimization

Read the original on arXiv Machine Learning →

The study evaluates whether providing biological context to large language models (LLMs) improves their performance in autonomous laboratory optimization. Using 45 biology tasks derived from 2,485 experimental data points, the authors compared domain‑aware prompting (with biological parameter names, units, and labels) to domain‑agnostic prompting (renamed to be non‑biological) across 19 LLMs over 30 iterations. Results show that domain‑agnostic prompts outperform domain‑aware prompts on 58.7% of tasks by trajectory and 61.9% by endpoint, suggesting that biological knowledge does not reliably enhance LLM‑guided experimental optimization. "whyItMatters":"The findings imply that autonomous laboratory loops should treat biology‑aware optimization as a distinct capability rather than relying on biological context to improve LLM performance."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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