Before You Ask the Judge, Read the Logits
TL;DR for operators A scientific agent that generates 16 plausible hypotheses does not need every candidate to receive the same expensive treatment. The operational problem is deciding which hypotheses deserve simulation, experiment, or expert attention first. Rajwal, Das, and Ghosal show that, on a benchmark built from 1,323 published papers, simply measuring how strongly open-weight models internally support each candidate can rank the published hypothesis substantially better than asking GPT-5 to compare all candidates explicitly.1 Pooled first-place accuracy was 33.1% using normalized token likelihood and 32.8% using raw model logits, versus 16.6% for the tested zero-shot GPT-5 judge. ...