Hypothesis Portfolio Search: Shared Feedback for Genetic Perturbation Discovery

LLM agents can effectively link biological knowledge to genetic perturbation experiments, yet tight budgets leave most plausible interventions untested. Active hit discovery aims to maximize the recovery of assay-specific hits within these limits. However, standard scoring methods select candidates purely by global rank, risking over-concentration on dominant pathways while neglecting under-explored biological mechanisms, whereas prompt-based LLM agents struggle to convert accumulating feedback into quantitative, assay-specific value estimates. To address this, we formulate discovery as two coupled decisions: allocating testing budgets across hypothesis-defined candidate pools, and selecting specific genes within each pool. We propose **Hy**pothesis **P**ortfolio **S**earch, termed **HyPS**, a novel framework that treats hypotheses as revisable search units with dynamic budget allocations. Feedback iteratively updates these units, while a shared lightweight scorer integrates historical responses with biological knowledge to rank untested candidates. We evaluate HyPS retrospectively on six single-gene perturbation screens using nine LLM backbones at matched testing budgets. Averaged equally across screens and backbones, HyPS raises overall hit recall from 8.40% to 12.47%, outperforming the same-backbone retrieval-augmented agent in 53 of 54 comparisons. On a two-gene perturbation benchmark, HyPS increases mean hit-pair recovery by 72.4% over the same comparator across three LLM backbones. Our code will be made publicly available upon publication.