We report a structural convergence among four influential theories of mind: Kahneman dual-system theory, Friston predictive processing, Minsky society of mind, and Clark extended mind, emerging unintentionally within a practical AI architecture known as Agentic Flow. Designed to address limitations of large language models LLMs, Agentic Flow comprises five interlocking modules - Retrieval, Cognition, Control, Action, and Memory - organized into a repeatable cognitive loop. Although originally inspired only by Minsky and Clark, subsequent analysis showed that its structure echoes computational motifs from all four theories. This suggests that theoretical convergence may arise from implementation constraints rather than deliberate synthesis. In controlled evaluations, the structured agent achieved 95.8 percent task success compared to 62.3 percent for baseline LLMs, demonstrating stronger constraint adherence and more reproducible reasoning. We characterize this convergence through a broader descriptive meta-architecture called PEACE, highlighting recurring patterns such as predictive modeling, associative recall, and error-sensitive control. Later formalized as the Structured Cognitive Loop (SCL), this abstraction generalizes principles first realized in Agentic Flow as a foundation for behavioral intelligence in LLM-based agents.Rather than asserting theoretical unification, this position paper proposes that intelligent architectures may evolve toward shared structural patterns shaped by practical demands. Agentic Flow thus functions as an implementation instance of the Structured Cognitive Loop, illustrating how a unified cognitive form can emerge not from abstraction, but from the necessities of real-world reasoning.

翻译：我们报告了四种有影响力的心智理论之间的结构趋同现象：卡尼曼双系统理论、弗里斯顿预测处理理论、明斯基心智社会理论和克拉克延展心智理论。这种趋同意外地出现在一个名为Agentic Flow的实用人工智能架构中。为克服大语言模型（LLMs）的局限性而设计的Agentic Flow包含五个相互锁定的模块——检索、认知、控制、行动和记忆，这些模块被组织成一个可重复的认知循环。尽管最初仅受明斯基和克拉克理论的启发，后续分析表明其结构同时呼应了所有四种理论的计算模式。这表明理论趋同可能源于实现约束而非刻意整合。在受控评估中，结构化智能体实现了95.8%的任务成功率，而基线LLMs仅为62.3%，表现出更强的约束遵循能力和更可复现的推理过程。我们通过一个更广泛的描述性元架构PEACE来刻画这种趋同，突显了预测建模、联想回忆和误差敏感控制等重复出现的模式。后续被形式化为结构化认知循环（SCL）的这一抽象框架，将最初在Agentic Flow中实现的原则推广为基于LLM智能体的行为智能基础。本立场论文并不主张理论统一，而是提出智能架构可能在实际需求的塑造下演化出共享的结构模式。因此，Agentic Flow作为结构化认知循环的一个实现实例，展示了统一的认知形态如何能够从现实世界推理的必要性中——而非从抽象理论中——自然涌现。