SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization
- Published
- Source
- arXiv
- Paper number
- 1417
- Field
- Research Agents
- arXiv ID
- 2611.08483
Key points
- SwarmEvolve is an evolutionary search framework in which swarms of low-cost agents reason in latent space and communicate by passing KV caches in each round.
- A novelty filter keeps the agents with the most distinct 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 and writes one new program.
- On 21 problems in mathematical discovery and scientific computing, it got 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.
- It evaluates 5 to 13 times more programs per hour than existing SOTA frameworks.
- With Qwen3 8B, it improves on the solutions that AlphaEvolve first found with frontier LLMs on three problems.
Paper links
External research summaries. These are not HDATF publications or measured product results.