Evaluating AI Research Agents: SwarmEvolve vs. Scientist World

Two arXiv v1 preprints, SwarmEvolve and Scientist World , measure different research-agent tasks : evolutionary program search and performance in paper-derived environments. Throughput and Evolutionary Search SwarmEvolve, authored by Qile Jiang and George Em Karniadakis, focuses on the efficiency of evolutionary program search. The system operates by deploying 10 swarms, each containing 45 latent agents per round. To maintain diversity, a novelty filter keeps 30 agents in each swarm; summarizers read their KV caches to synthesize candidate programs. This mechanism is tested across 21 mathematical and scientific-computing problems. The researchers tested Qwen3 8B, 14B and 32B-FP8 models, using three seeds and 60-minute runs on one GPU with eight CPU cores.

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