SwarmEvolve 用 450 个隐空间智能体并行做数学发现与优化

"SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization"

One of the strangest bottlenecks in AI-assisted mathematical discovery is that AI agents spend too much time writing out their reasoning instead of actually exploring new solutions.

So this paper introduces SwarmEvolve, which runs 450 agents in parallel that reasons internally without generating text and shares their ideas through KV-caches. Then a novelty filter preserves diverse approaches before combining them into new solutions.

Across 21 mathematical and scientific problems, SwarmEvolve was able to explore 5-13x more programs per hour and reaches competing systems' 60-minute results in just 3–8 minutes.

Using only Qwen3-8B, it even improves three solutions originally found by AlphaEvolve with frontier LLMs.

https://www.alphaxiv.org/abs/2611.08483