CARE: Controlling LLM-Generated Policies through Auditable Review of Evidence in Scientific Experimentation

High-throughput scientific experimentation — screening thousands of chemical compounds, for instance — is expensive and irreversible, making it a dangerous domain for unconstrained AI autonomy. CARE solves this by keeping a proven non-LLM optimizer as the default while allowing an LLM to propose challenger strategies, only authorizing the challenger when pre-outcome evidence actually supports the switch. Every decision is logged in an auditable trail. On chemistry benchmarks, this outperforms all other evaluated methods, improving best-found outcomes significantly over a strong baseline. Applications extend to drug discovery, materials science, process optimization in manufacturing, and any high-stakes experimental domain where AI creativity needs to be harnessed without sacrificing accountability or safety. Authors: Guanyu Liu, Weiyi Kong, Zeyu Wang, Boer Zhang, Baiqing Li, Peiyu Zhang, Tianyu Shi Paper: https://arxiv.org/abs/2606.14581v1