Agents in the Lab: When Bayesian Adversaries Keep AI Scientists Honest
Lab work has an old rule: never trust the first beautiful result. It may be correct. It may also be a measurement artifact wearing a lab coat. That rule becomes more important when the “research assistant” is an LLM that can write code, invent tests, explain errors, and occasionally hallucinate with the confidence of a junior consultant who has just discovered PowerPoint. The paper “AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework” takes this problem seriously.1 Its central claim is not that scientific automation needs a larger model, a longer prompt, or another cheerful agent named “Planner.” The claim is sharper: in AI-assisted scientific coding, both the generated code and the generated tests are uncertain. If the validator is also an LLM, then the system has not solved hallucination. It has merely hired hallucination as compliance staff. ...