Agent swarms communicating in latent space without text generation can solve complex optimization problems 7-20x faster than traditional LLM-guided search, even with smaller models.
SwarmEvolve uses hundreds of lightweight AI agents working together in swarms to improve programs for math and science problems. Instead of generating long text explanations, agents reason directly in latent space and share information through KV caches, with a novelty filter ensuring diverse ideas. The system is 7-20x faster than existing methods while using smaller models.