SwarmEvolve uses latent space agent swarms for faster program optimization
Original title: SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization
SwarmEvolve reaches best solutions in 3-8 minutes via latent-space KV cache sharing instead of 60 minutes.
SwarmEvolve employs swarms of low-cost agents that reason in latent space by sharing KV caches. A novelty filter and summarizer agent enable efficient idea crossover. On 21 mathematical discovery problems, it achieves highest scores in median 3-8 minutes and improves on AlphaEvolve with Qwen3 8B.
- SwarmEvolve improves on AlphaEvolve solutions with Qwen3 8B
- Uses direct distance measurement between hidden state vectors
- Agent swarms study problems from different angles
- Organizations
- Hugging Face
- AI models
- Qwen3 8B
From the original article
1 min read at huggingface.co
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.
By Qile Jiang, George Em Karniadakis · huggingface.co
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