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
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 caches. Each swarm studies the problem from a different angle. A novelty filter keeps the most distinct agent ideas by directly measuring the…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization was first seen on Hugging Face · Daily papers on Nov 7, 2026. Since then it has appeared in 17 checks on 2 different days. Its best position was #23 of 30, reached on Nov 8, 07:28 UTC. It is #24 right now, slipping from #23 to #24 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #24, slipping from #23 to #24 over the last 6 checks.
#23 on Hugging Face · Daily papers, on Nov 8, 07:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 7, 2026 and has been on the list on 2 days (17 checks) since.