提出可执行的认知循环框架，让AI具备主动理解能力
Executable Epistemology: The Structured Cognitive Loop as an Architecture of Intentional Understanding
- 构建认知循环结构，将哲学思想转化为可计算的智能流程
- 实验证明分离式架构比单一提示系统更清晰可解释
- 适合研究意识、智能本质或追求可解释AI的学者
大型语言模型表现出智能却缺乏真正的认知理解，暴露了认知架构的缺失。本文提出结构化认知循环（SCL），作为涌现智能的可执行认识论框架。不同于传统人工智能关注‘什么是智能’（本体论），SCL聚焦‘认知在何种条件下涌现’（认识论）。基于心灵哲学与认知现象学，结合过程哲学、具身认知和延伸心智理论，将智能定义为判断、记忆、控制、行动与调节的持续循环过程。SCL有三方面贡献：其一，将哲学洞见转化为可计算结构，实现‘可执行认识论’；其二，证明认知架构的功能分离能产生更连贯、可解释的行为，经由智能体评估验证；其三，重新定义智能——不在于表征准确性，而在于通过有意理解重构自身认知状态的能力。该框架推动心灵哲学、认识论与人工智能发展，使认知理论可被实施与检验，将行为根基从统计规律转向认知结构。它强调知识非真理占有，而是现象学一致循环中的持续重建。本文将其置于认知现象学、涌现性、规范性与意向性等讨论中，主张真正进步需结构化架构而非更大模型。
原文摘要 · Abstract (English)
Large language models exhibit intelligence without genuine epistemic understanding, exposing a key gap: the absence of epistemic architecture. This paper introduces the Structured Cognitive Loop (SCL) as an executable epistemological framework for emergent intelligence. Unlike traditional AI research asking "what is intelligence?" (ontological), SCL asks "under what conditions does cognition emerge?" (epistemological). Grounded in philosophy of mind and cognitive phenomenology, SCL bridges conceptual philosophy and implementable cognition. Drawing on process philosophy, enactive cognition, and extended mind theory, we define intelligence not as a property but as a performed process -- a continuous loop of judgment, memory, control, action, and regulation. SCL makes three contributions. First, it operationalizes philosophical insights into computationally interpretable structures, enabling "executable epistemology" -- philosophy as structural experiment. Second, it shows that functional separation within cognitive architecture yields more coherent and interpretable behavior than monolithic prompt based systems, supported by agent evaluations. Third, it redefines intelligence: not representational accuracy but the capacity to reconstruct its own epistemic state through intentional understanding. This framework impacts philosophy of mind, epistemology, and AI. For philosophy, it allows theories of cognition to be enacted and tested. For AI, it grounds behavior in epistemic structure rather than statistical regularity. For epistemology, it frames knowledge not as truth possession but as continuous reconstruction within a phenomenologically coherent loop. We situate SCL within debates on cognitive phenomenology, emergence, normativity, and intentionality, arguing that real progress requires not larger models but architectures that realize cognitive principles structurally.
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