Towards Diverse Scientific Hypothesis Search with Large Language Models

The paper presents a novel evolutionary framework for hypothesis generation using large language models, termed \ours, which addresses the limitations of traditional optimization-focused methods that lead to diversity collapse. By employing a parallel tempering approach, \ours enables efficient exploration of diverse, high-quality hypotheses across multiple domains, including molecular and algorithm discovery, while adhering to a fixed validation budget. This method enhances both the quality and diversity of hypotheses generated, making it a valuable tool for practitioners aiming to mitigate uncertainty in scientific discovery processes.