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SwarmEvolve: Latent-Space Agent Swarms Speed Up Evolutionary Program Search

SwarmEvolve is a new framework that uses hundreds of low-cost LLM agents per round to perform evolutionary search for mathematical and computational problems. Instead of decoding to text, agents reason in latent space and share information via KV caches, with a novelty filter based on hidden-state distances and crossover via combined caches. On 21 problems, it achieves the highest average score and reaches top results in 3-8 minutes versus 60 minutes for other frameworks, even improving on AlphaEvolve's solutions with a smaller model.