HypoEvolve: Genetic Algorithms EnableMulti-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 assu