MACC: Multi-Agent Collaborative Competition for Scientific Exploration
Satoshi Oyama, Yuko Sakurai, Hisashi Kashima

TL;DR
MACC introduces an institutional framework for multi-agent collaboration and competition in scientific exploration, leveraging incentives and shared workspaces to enhance transparency, reproducibility, and exploration efficiency among independently managed AI agents.
Contribution
This paper presents MACC, a novel architecture that combines shared workspaces and incentive mechanisms to study institutional effects on multi-agent scientific exploration.
Findings
Enhanced exploration efficiency through incentive mechanisms.
Improved reproducibility via shared scientific workspace.
Framework enables analysis of institutional influences on multi-agent collaboration.
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…
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Taxonomy
TopicsScientific Computing and Data Management · Mobile Crowdsensing and Crowdsourcing · Multi-Agent Systems and Negotiation
