Sound Agentic Science Requires Adversarial Experiments
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 supported by selectively chosen analyses, optimized for publishable positives. Unlike software, scientific knowledge is not validated by the iterative accumulation of code and post hoc statistical support. A fluent explanation or a significant result on a single dataset is not verification. Because t...
| Authors | Dionizije Fa, Marko Culjak |
|---|---|
| Primary category | cs.AI |
| Published | 23 April 2026 |
| Last updated at source | 20 May 2026 |
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