Emergent Cognitive Convergence via Implementation: A Structured Loop Reflecting Four Theories of Mind – A Position Paper

We report the discovery of a meaningful structural convergence among four influential theories of mind—Kahneman's dual-system theory, Friston's predictive processing, Minsky's society of mind, and Clark's extended mind—through their unintentional but systematic emergence in a practical AI agent architecture called Agentic Flow. Originally designed to overcome critical limitations of large language models (LLMs), this architecture consists of five interlocking modules—Retrieval, Cognition, Control, Memory, and Action—organized into a repeatable cognitive loop. Although initially inspired only by Minsky and Clark's frameworks, our structural analysis reveals that Agentic Flow partially mirrors core computational patterns described by each of the four theories, suggesting a limited but notable structural convergence. To evaluate this convergence, we conducted controlled experiments comparing structured agents to baseline LLM-based systems across multi-step, conditional reasoning tasks. The structured agent consistently achieved 95.8% task success and demonstrated robust constraint adherence, whereas the baseline system exhibited a 62.3% success rate under the same conditions. While based on a proprietary implementation, evaluation protocols are made available for verification. These experiments were not conducted merely to demonstrate performance advantages, but to qualitatively reveal how theoretical convergence can arise organically through implementation demands rather than deliberate design.We present PEACE as a descriptive meta-architecture that retrospectively draws attention to recurring computational patterns—such as predictive modeling, associative recall, and error-sensitive control—identified in Agentic Flow. Rather than offering a formal theory or aiming to supplant existing frameworks, PEACE serves as a practical abstraction that surfaces design-level regularities seen across otherwise divergent cognitive architectures. Its value lies not in theoretical unification, but in framing a shared vocabulary for analyzing and constructing cognitive systems shaped by real-world implementation demands.Our findings have implications for cognitive science, artificial intelligence, and philosophy of mind. They suggest that intelligent architectures may evolve toward shared structural patterns, shaped not by theory but by the demands of real-world reasoning under uncertainty. We argue that Agentic Flow may offer a partial instantiation—or structural echo—of Newell’s long-sought "unified theory of cognition," discovered not through abstraction, but through necessity. By ‘partial instantiation,’ we do not imply theoretical alignment but structural features independently anticipated by multiple cognitive theories. We emphasize that the observed convergence reflects architectural motifs rather than theoretical completeness.This paper should be read as a position paper—an exploratory reflection on how implementation can surface latent architectural commonalities across divergent cognitive theories, rather than a claim of theoretical unification.