HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

Scientific agents increasingly contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems bring scientific agents and evolutionary search together to develop hypotheses through cycles of critique, comparison, and revision. However, how different forms of agent collaboration affect hypothesis quality remains an open question. Answering this question requires separating the effects of agents’ scientific capabilities from those of their collaboration. A suitable framework must therefore preserve the agents’ scientific roles and support different rules for combining, revising, and retaining hypotheses. Building on this perspective, we introduce HypoEvolve, which makes collaboration explicit through successive updates to a hypothesis population. Specifically, we propose to use a generational genetic algorithm to coordinate specialized large language model (LLM) agents that integrate mechanistic arguments, reconsider assumptions, and assess evidence and testability. Each generation specifies how scientific judgments and new proposals reshape the population, which makes the effects of collaboration on hypothesis quality directly testable.

Introduction. Large language models (LLMs) are enabling scientific agents to formulate hypotheses that connect existing evidence to new research directions [13, 42, 49]. They can synthesize findings across studies into explicit scientific claims and supporting rationales that connect proposed relationships to the available evidence [2, 11]. These capabilities open a path to systems that develop scientific ideas through repeated examination of hypotheses and their supporting evidence [10, 12, 13]. Recent progress in automated discovery spans scientific-agent workflows and evolutionary search. One line of research develops agents that ground proposals in the literature and refine them through critical feedback [2, 11, 42]. Another uses evolutionary search to develop LLM-generated programs, equations, and molecules, with evaluation and selection guiding subsequent exploration [28, 31, 41]. Recent systems bring these directions together for scientific hypotheses through tournamentbased evolution and hierarchical refinement [13, 48].

Discussion / Conclusion. HypoEvolve formulates hypothesis development as a population-search problem for scientific agents. We couple reasoning over scientific claims and rationales with a generational genetic algorithm that directs selection, variation, and replacement. This formulation gives each agent contribution a defined role in the search and makes the rules of collaboration available for controlled study. Our drug repurposing evaluation connects the generated explanations to target-level biological evidence from two complementary sources. Across 34 cancer types, HypoEvolve achieves the highest mean scores among six baselines on both DepMap selectivity and Open Targets association. On the shared 26-type DepMap panel, selectivity is 0.171, compared with 0.039 for single-pass generation. Crossover or mutation produces 87 of the 94 final hypotheses. The parent-selection study directly tests how a coordination decision affects hypothesis quality.