hypothesis-search

This paper introduces Factorized Hypothesis Search (FHS), a method for retrieving concepts from large taxonomies when inputs provide indirect contextual evidence, such as table cells or clinical notes. FHS achieves strong results on financial taxonomy tagging and CodiEsp clinical coding, outperforming non-oracle baselines in Recall@1, MRR, and accuracy.

This paper addresses the limitation of static surprisal in LLM-based scientific discovery by introducing evidence-informed non-stationary beliefs, and proposes belief-update filtering and diversity maximization to improve discovery, achieving 30.62% higher non-stationary surprisal across five domains.