Current generative AI systems are increasingly effective at processing explicit knowledge, including retrieving information, summarising documents, generating explanations, and supporting codified workflows. However, high-level expertise also depends on tacit sensing: perceiving weak signals, recognising emerging tensions, detecting coherence degradation, and anticipating instability before formal indicators appear. Existing AI education, AI literacy, and human-AI collaboration frameworks remain centred on prompting, task execution, and productivity support and are poorly equipped to address this tacit layer of expert cognition. This vision paper argues that next-generation AI systems should move beyond explicit knowledge processing toward the longitudinal modelling of expert tacit sensing. It introduces Tacit Signal Infrastructure as a layer for capturing, structuring, modelling, interpreting, and validating expert tacit signals over time. It further defines Long-term Cognitive Operations as the practices required to maintain and govern such systems, including memory curation, semantic organisation, tacit signal modelling, reasoning calibration, and cognitive governance. Building on this framing, the paper proposes the Cognitive Operations Manager as a prototype AI-native professional role for coordinating tacit signal modelling, semantic modelling, AI system calibration, expert validation, and ethical governance. It also introduces the Cognitive Operations Research and Training Framework (CORTF) to support research, education, and workforce development. The paper contributes a conceptual foundation for designing AI systems that model expert sensing over time, positioning cognition as an infrastructural, operational, and professional domain in persistent human-AI systems.

翻译：当前生成式AI系统在处理显性知识方面日益高效，包括信息检索、文档摘要、解释生成及支持流程化工作流。然而，高水平专家能力同样依赖隐性感知：捕捉微弱信号、识别新兴张力、察觉连贯性退化，以及在正式指标出现前预判不稳定性。现有的AI教育、AI素养及人机协作框架仍以提示工程、任务执行和生产力支持为中心，难以应对专家认知的这一隐性层面。本文提出愿景：下一代AI系统应超越显性知识处理，转向对专家隐性感知的纵向建模。我们引入"隐性信号基础设施"作为随时间捕获、结构化、建模、解释与验证专家隐性信号的层级，并进一步定义"长期认知操作"作为维护与治理此类系统所需实践，涵盖记忆策展、语义组织、隐性信号建模、推理校准及认知治理。基于此框架，本文提出"认知操作经理"作为原型化AI原生专业角色，协调隐性信号建模、语义建模、AI系统校准、专家验证与伦理治理，同时引入"认知操作研究与训练框架（CORTF）"以支持研究、教育与人才培养。本文为设计随时间建模专家感知的AI系统奠定了概念基础，将认知定位为持久人机系统中的基础设施、操作与专业领域。