An Empirical Study of Multi-Agent Collaboration for Automated Research
As AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cognitive bottlenecks in automated research. However, the optimal multi-agent coordination framework for these autonomous agents remains largely unexplored. In this paper, we present a systematic empirical study investigating the comparative efficacy of distinct multi-agent structures for automated machine learning optimization. Utilizing a rigorously controlled, execution-based testbed equipped with Git worktree isolation and explicit global memory, we benchmark a single-agent baseline against two multi-agent paradigms: a subagent architecture (parallel...
| Authors | Yang Shen, Zhenyi Yi, Ziyi Zhao et al. |
|---|---|
| Primary category | cs.MA |
| Published | 31 March 2026 |
| Last updated at source | 9 May 2026 |
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