HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses
HypoEvolve uses a generational genetic algorithm coordinating specialized LLM agents to generate scientific hypotheses, outperforming six baselines on cancer drug repurposing.
HypoEvolve is a framework that coordinates specialized LLM agents through a generational genetic algorithm to produce, revise, and retain scientific hypotheses with explicit collaboration roles. It evaluates drug repurposing hypotheses against external evidence from DepMap and Open Targets across 34 cancer types. It achieves the highest scores against six baselines, reaching DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains over single-pass generation generalize to held-out cancer types.
- Coordinates specialized LLM agents via a generational genetic algorithm over a hypothesis population.
- Evaluated on drug repurposing across 34 cancer types using DepMap and Open Targets evidence.
- DepMap selectivity of 0.171 versus 0.115 for the strongest baseline.
- Gains over single-pass generation generalize to held-out cancer types.
- Tags
- autonomous-sciencecancer-researchdrug-repurposinggenetic-algorithmshypothesis-generationllm-agentsmulti-agent-systems
- Organizations
- DepMapOpen Targets
From the original article
1 min read at huggingface.co
Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems combine scientific agents with evolutionary search through critique, comparison, and revision. However, how different forms of agent collaboration affect hypothesis quality remains an open question.
By Jieyuan Liu, Mengzhou Hu, Jefferson Chen · huggingface.co
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