Recursive Cognitive Architecture: Toward Physically Grounded Artificial General Intelligence Through Fractal Distribution and Embodied Learning

We introduce the Recursive Cognitive Architecture (RCA), a distributed framework for Artificial General Intelligence in which intelligence emerges from the dynamic orchestration of a standardized unit, the Cognitive Node, that operates identically across all scales of deployment, from edge-embedded robotic manipulators to centralized cloud cores. Each node integrates four subsystems: a Perception Encoder, a World Model, a Policy Engine, and a Metacognitive Critic. This fractal consistency enables fluid scaling without translation layers between hardware tiers, allowing the system to collapse from a fully distributed configuration to a single computational substrate without architectural changes.

RCA is grounded in the thesis that general intelligence requires physical embodiment: the capacity to form predictions about the world, act on them, and update internal representations based on the discrepancy between expectations and outcomes. We argue that the Symbol Grounding Problem, the inability of purely symbolic systems to anchor their representations in physical reality, is an architectural limitation that cannot be resolved by scaling, and that physics itself provides a superior loss function to human feedback for training a system of general capability.

RCA introduces three principal contributions. First, Entropy-Based Routing, a mechanism by which each node measures its own semantic uncertainty and escalates unresolved cognitive tasks to higher-level nodes rather than hallucinating a response. Second, Self-Supervised Physics, a learning paradigm in which sensory prediction error replaces human feedback as the primary loss signal, grounding the system's knowledge in the immutable laws of physical reality. Third, Proof of Reality, a decentralized economic protocol that incentivizes the global, distributed collection of sensorimotor experience by rewarding contributors proportionally to the reduction in systemic uncertainty they produce, transforming the cost of physical data collection into a self-sustaining distributed incentive.

Together, these contributions define an architectural hypothesis for scalable physically grounded intelligence that is not merely linguistic but causally aware, physically grounded, and scalable through a global ecosystem of heterogeneous hardware agents.

RCA builds on and extends the Free Energy Principle of Friston, the hierarchical predictive framework of Hawkins, and the Joint Embedding Predictive Architecture of LeCun, while contributing the specific mechanisms of collapsibility, entropy-driven escalation, distributed economic grounding, and irreversibility-based safety that make physical AGI tractable at global scale.