让AI科学家具备人类研究者独特的认知与集体知识，实现更深层的科学推理。
MirrorMind: Empowering OmniScientist with the Expert Perspectives and Collective Knowledge of Human Scientists
- 构建分层认知架构，融合个体研究记忆与学科知识网络。
- 在四类任务中显著提升跨领域协作与个性化推理能力。
- 适合对科学智能、认知建模感兴趣的科研与工程人员。
AI科学家在自动化科研方面展现出巨大潜力，但现有方法多将科学发现视为孤立的优化或搜索过程，忽略了知识生产本质上是社会性和历史性的活动。人类科学洞察源于两个相互关联的来源：一是个体认知轨迹，即研究者自身研究历程和风格偏好塑造的独特见解；二是集体学科记忆，即知识通过引用和概念网络沉淀形成的结构化体系。现有大模型仍难以有效表征这种高保真度的认知与社会背景。为此，我们提出MirrorMind，一种分层认知架构，包含三个层级：个体层通过捕捉事件、语义和人格记忆，构建高保真个体研究者模型；领域层将集体知识映射为结构化的学科概念图；跨领域层作为正交协调引擎。关键在于，该架构分离记忆存储与代理执行，使AI科学家可灵活调用个体记忆以获取独特视角，或利用集体结构进行推理。我们在四个综合任务上评估了MirrorMind，包括作者级认知模拟、互补推理、跨领域协作促进及多代理科学问题求解。结果表明，通过融合个体认知深度与集体学科广度，MirrorMind超越了简单事实检索，实现了结构性、个性化且能生成洞见的科学推理。
原文摘要 · Abstract (English)
The emergence of AI Scientists has demonstrated remarkable potential in automating scientific research. However, current approaches largely conceptualize scientific discovery as a solitary optimization or search process, overlooking that knowledge production is inherently a social and historical endeavor. Human scientific insight stems from two distinct yet interconnected sources. First is the individual cognitive trajectory, where a researcher's unique insight is shaped by their evolving research history and stylistic preferences; another is the collective disciplinary memory, where knowledge is sedimented into vast, interconnected networks of citations and concepts. Existing LLMs still struggle to represent these structured, high-fidelity cognitive and social contexts. To bridge this gap, we introduce MirrorMind, a hierarchical cognitive architecture that integrates dual-memory representations within a three-level framework. The Individual Level constructs high-fidelity cognitive models of individual researchers by capturing their episodic, semantic, and persona memories; the Domain Level maps collective knowledge into structured disciplinary concept graphs; and the Interdisciplinary Level that acts as an orthogonal orchestration engine. Crucially, our architecture separates memory storage from agentic execution, enabling AI scientist agents to flexibly access individual memories for unique perspectives or collective structures to reason. We evaluate MirrorMind across four comprehensive tasks, including author-level cognitive simulation, complementary reasoning, cross-disciplinary collaboration promotion, and multi-agent scientific problem solving. The results show that by integrating individual cognitive depth with collective disciplinary breadth, MirrorMind moves beyond simple fact retrieval toward structural, personalized, and insight-generating scientific reasoning.
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