Collective Intelligence: On the Promise and Reality of Multi-Agent Systems for AI-Driven Scientific Discovery

Modern scientific progress is increasingly driven by collaborative endeavors that leverage specialized expertise and constructive peer critique. Multi-agent systems (MAS) offer a robust framework to emulate these collaborative dynamics inherent to human researcher teams by combining distributed information processing with discussion-driven validation, enabling collective intelligence that exceeds the capabilities of individual agents in addressing complex interdisciplinary challenges. We introduce an application-oriented taxonomy that maps canonical stages of the research workflow to both the promise and the current reality of MAS in scientific discovery, providing a coherent foundation for understanding, evaluating, and advancing autonomous AI co-scientists. We highlight the distinctive advantages of MAS over single-agent approaches, identify key bottlenecks limiting current deployments, and outline critical research frontiers to bridge the gap between potential and practice. We argue that MAS hold transformative promise to move beyond the role of assistive tools, evolving into autonomous co-scientists capable of parallel exploration of vast knowledge spaces and robust validation through diverse perspectives, thereby advancing open-ended scientific research in partnership alongside human investigators.