[2603.03780] MACC: Multi-Agent Collaborative Competition for Scientific Exploration

Computer Science > Multiagent Systems arXiv:2603.03780 (cs) [Submitted on 4 Mar 2026] Title:MACC: Multi-Agent Collaborative Competition for Scientific Exploration Authors:Satoshi Oyama, Yuko Sakurai, Hisashi Kashima View a PDF of the paper titled MACC: Multi-Agent Collaborative Competition for Scientific Exploration, by Satoshi Oyama and 2 other authors View PDF HTML (experimental) Abstract:Scientific discovery still relies heavily on the manual efforts of individual researchers, leading to limited exploration, redundant trials, and reduced reproducibility. Human-participant data analysis competitions generate diverse approaches, yet fluctuations in participation and the lack of independent repetitions show that parallel exploration alone is insufficient for achieving reliable scientific inquiry. As advanced AI agents based on large language models (LLMs) increasingly perform analytical tasks, relying on a single highly capable agent is unlikely to overcome these structural limitations. Recent work has begun to explore how multiple LLM-based agents can collaborate or compete in scientific workflows-a growing trend we refer to as MA4Science. However, most existing MA4Science studies assume that all agents are controlled by a single organizational entity, limiting their ability to examine how institutional mechanisms-such as incentives, information sharing, and reproducibility-shape collective exploration among independently managed agents. To address this gap, we introduce ...