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.

翻译：多智能体系统已成为自动化科学发现的有力范式。为区分多智能体系统中的智能体行为，现有框架通常分配诸如"评审者"或"撰写者"等通用角色化身份，或依赖基于粗粒度关键词的身份设定。这种方法虽具功能性，但过度简化了人类科学家的运作方式——其贡献往往由独特的研究轨迹塑造。为此，我们提出INDIBATOR框架，该框架通过两种模态构建的个体化科学家档案为智能体奠定基础：表征文献知识的发表历史与表征结构先验的分子研究历史。这些智能体通过提案、批判和投票阶段进行多轮辩论。评估结果表明，基于细粒度个体性建模的智能体系统持续优于依赖粗粒度身份设定的系统，达到竞争性或最先进的性能水平。这些结果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。