SwarmEvolve 论文提出潜空间智能体群体框架加速进化搜索
SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization
AI 导读
Qile Jiang 与 George Em Karniadakis 在 arXiv 论文（arXiv:2611.08483）中提出 SwarmEvolve，让每轮数百个低成本智能体在潜空间推理、通过传递 KV cache 共享信息，无需解码为文本。
正文 · 原文
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 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.
| Subjects: | Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2611.08483 [cs.AI] |
| (or arXiv:2611.08483v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08483 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qile Jiang [view email]
[v1]
Thu, 5 Nov 2026 18:59:29 UTC (1,952 KB)
来源：arXiv cs.AI · arxiv.org