arXiv: cs.AI preprintsInternational6 November 2026
SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization
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arXiv:2611.08483v1 Announce Type: new Abstract: LLM-guided evolutionary search uses AI agents to improve programs for mathematical and computational problems by iteratively refining solutions and combining high-scoring methods. However, the process is often slow because the LLM generates a long textual chain of thought for each round and must repeat this generation many times to maintain population diversity. We present SwarmEvolve, a framework in which hundreds of low-cost agents per round work in swarms, reason in latent space without decoding to text, and share information by passing KV cac
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