[2603.29632] An Empirical Study of Multi-Agent Collaboration for Automated Research

Computer Science > Multiagent Systems arXiv:2603.29632 (cs) [Submitted on 31 Mar 2026] Title:An Empirical Study of Multi-Agent Collaboration for Automated Research Authors:Yang Shen, Zhenyi Yi, Ziyi Zhao, Lijun Sun, Dongyang Li, Chin-Teng Lin, Yuhui Shi View a PDF of the paper titled An Empirical Study of Multi-Agent Collaboration for Automated Research, by Yang Shen and 6 other authors View PDF HTML (experimental) Abstract: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 exploration with post-hoc consolidation) and an agent team architecture (experts with pre-execution handoffs). By evaluating these systems under strictly fixed computational time budgets, our findings reveal a fundamental trade-off between operational stability and theoretical deliberation. The subagent mode functions as a highly resilient, high-throughput search engin...