Krish Ghosh

Recursive Priced Falsification: Asymmetric Trials with a Forecast-Scored Planner for Autonomous Discovery

Autonomous discovery loops, in which language-model agents improve a design against an automatic evaluator, spend most of their effort on ideas that fail. We argue that such a loop should be built so that a failure is cheap and a success is not capped, and that the part that decides what to try should be scored like ev...