Sound Agentic Science Requires Adversarial Experiments

ArXi:2604.22080v1 Announce Type: new LLM-based agents are rapidly being adopted for scientific data analysis, automating tasks once limited by human time and expertise. This capability is often framed as an acceleration of discovery, but it also accelerates a familiar failure mode, the rapid production of plausible, endlessly revisable analyses that are easy to generate, effectively turning hypothesis space into candidate claims ed by selectively chosen analyses, optimized for publishable positives.