RESEARCHarXiv CS.AI·4/27/2026
Sound Agentic Science Requires Adversarial Experiments
LLM-based agents are increasingly used in scientific data analysis, but risk generating plausible analyses optimized for publishable positive results. This paper proposes that non-experimental claims produced with agentic assistance be evaluated under a falsification framework to ensure scientific rigor.
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