INDIBATOR：分子发现多智能体辩论中的多样化与事实导向个体性

摘要

多智能体系统已成为自动化科学发现的重要范式。为区分系统中各智能体的行为，现有框架通常采用基于通用角色的设定（如"评审员"或"撰稿人"），或依赖粗粒度的关键词标签。虽然功能可行，但这种方法过度简化了人类科研人员的工作方式——他们的学术贡献实则由其独特的研究轨迹塑造而成。为此，我们提出INDIBATOR分子发现框架，通过双模态数据为智能体构建个性化科学家画像：基于文献知识的发表历史记录和基于结构先验的分子研究历程。这些智能体通过提案、批判和投票三个阶段展开多轮辩论。评估结果表明，基于细粒度个体特征的智能体系统持续优于粗粒度角色设定方案，达到具有竞争力或最先进的性能水平。这些发现验证了捕捉个体智能体"科学DNA"对实现高质量科学发现的重要性。

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.

INDIBATOR：分子发现多智能体辩论中的多样化与事实导向个体性

INDIBATOR: Diverse and Fact-Grounded Individuality for Multi-Agent Debate in Molecular Discovery

摘要

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