MACC: Multi-Agent Collaborative Competition for Scientific Exploration
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...
| Authors | Satoshi Oyama, Yuko Sakurai, Hisashi Kashima |
|---|---|
| Primary category | cs.MA |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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