SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization

Qile Jiang, George Em Karniadakis

SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization: 12 upvotes on Hugging Face Daily Papers, #15 of 32 papers on 2026-11-06. Day-by-day upvote history.

Upvotes over time

It had 3 upvotes by its first day, 8 upvotes by the next day, 11 upvotes by day 3, 12 upvotes by day 7, and has 12 now. It ranked #99 of 207 papers in its week and #148 of 340 in its month. Its code repository has 44 GitHub stars.

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 distances between the agents' hidden state vectors. Crossover of ideas happens naturally in latent space, as a summarizer agent receives the combined KV caches of all swarm members. On 21 problems in mathematical discovery and scientific computing, SwarmEvolve gets the highest average score and needs a median of only 3 to 8 minutes to reach the best scores that other state-of-the-art frameworks reach in 60 minutes. On several problems, SwarmEvolve with Qwen3 8B improves on the solutions that AlphaEvolve first found with frontier LLMs. Our work shows that agent swarms that communicate in latent space offer a fast, low-cost approach to program search for scientific problems.

Paper page on Hugging Face · arXiv

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