让分子发现中的智能体拥有真实科学家的个人特质，提升协作创新质量。
INDIBATOR: Diverse and Fact-Grounded Individuality for Multi-Agent Debate in Molecular Discovery
- 基于文献与分子历史构建科学家个性化档案，实现细粒度角色差异化。
- 多轮辩论机制下，个体化智能体在分子发现任务中表现优于传统粗粒度角色。
- 适合需要模拟真实科研协作的药物研发与生成式化学研究场景。
多智能体系统已成为自动化科学发现的强大范式。当前框架通常为智能体分配通用角色如“评审者”或“撰写者”，或依赖粗粒度关键词角色，虽具功能性，但简化了人类科学家的真实贡献模式——其工作受个人研究历程深刻影响。为此，我们提出INDIBATOR框架，通过两个模态构建分子发现中的个体化科学家档案：从文献历史获取知识背景，从分子历史获取结构先验。这些智能体通过提案、批评与投票三阶段进行多轮辩论。评估表明，基于细粒度个体性的智能体持续优于依赖粗粒度角色的系统，在性能上达到竞争性或最先进水平。结果验证了捕捉智能体的“科学基因”对高质量发现至关重要。
原文摘要 · Abstract (English)
Multi-agent systems have emerged as a powerful paradigm for automating scientific discovery. To differentiate agent behavior in the multi-agent system, current frameworks typically assign generic role-based personas such as ''reviewer'' or ''writer'' or rely on coarse grained keyword-based personas. While functional, this approach oversimplifies how human scientists operate, whose contributions are shaped by their unique research trajectories. In response, we propose INDIBATOR, a framework for molecular discovery that grounds agents in individualized scientist profiles constructed from two modalities: publication history for literature-derived knowledge and molecular history for structural priors. These agents engage in multi-turn debate through proposal, critique, and voting phases. Our evaluation demonstrates that these fine-grained individuality-grounded agents consistently outperform systems relying on coarse-grained personas, achieving competitive or state-of-the-art performance. These results validate that capturing the ``scientific DNA'' of individual agents is essential for high-quality discovery.
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