让显微镜从看图工具变成能思考的科研伙伴。
Reconceptualizing Smart Microscopy: From Data Collection to Knowledge Creation by Multi-Agent Integration
- 用多智能体架构打通观察与认知的鸿沟
- 提出六项设计原则实现主动科学探究
- 适合想构建智能科研系统的研究人员
智能显微镜正推动生物成像范式变革，从被动观测工具转向科学探究的主动合作者。得益于自动化、算力和人工智能的进步，这类系统已具备自适应决策与实时实验控制能力。本文提出一个理论框架，将智能显微镜重新定义为科学探索的协作伙伴。核心是‘认识论-经验论鸿沟’——可观测现象（经验域）与需理解的本质（认识域）之间的差距。我们提出六大设计原则：认识-经验意识、层级上下文整合、从检测到感知的演进、自适应测量框架、叙事合成能力与跨情境推理。这些原则共同指导一个多智能体架构，使经验观察与科学理解目标对齐。该框架为构建超越自动化的显微系统提供路线图，使其可主动支持假说生成、洞察发现与理论发展，重新定义科研仪器在知识创造中的角色。
原文摘要 · Abstract (English)
Smart microscopy represents a paradigm shift in biological imaging, moving from passive observation tools to active collaborators in scientific inquiry. Enabled by advances in automation, computational power, and artificial intelligence, these systems are now capable of adaptive decision-making and real-time experimental control. Here, we introduce a theoretical framework that reconceptualizes smart microscopy as a partner in scientific investigation. Central to our framework is the concept of the 'epistemic-empirical divide' in cellular investigation-the gap between what is observable (empirical domain) and what must be understood (epistemic domain). We propose six core design principles: epistemic-empirical awareness, hierarchical context integration, an evolution from detection to perception, adaptive measurement frameworks, narrative synthesis capabilities, and cross-contextual reasoning. Together, these principles guide a multi-agent architecture designed to align empirical observation with the goals of scientific understanding. Our framework provides a roadmap for building microscopy systems that go beyond automation to actively support hypothesis generation, insight discovery, and theory development, redefining the role of scientific instruments in the process of knowledge creation.
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