Collective Cognitive Circuit: A Multi-Layered Distributed AI Architecture with Resource-Based Selection, Probabilistic World Map, and External Verification

This paper proposes the Collective Cognitive Circuit — a multi-layered distributed AI architecture designed to address fundamental limitations of current large language models, including catastrophic forgetting, absence of long-term memory, self-contamination during self-learning, and inability to distinguish truth from plausibility. The central thesis is that strong-form intelligence does not emerge within a single computational node but within a system of interacting specialized agents with role separation, hypothesis competition, and external verification — analogous to how collective human knowledge production operates through science. The architecture introduces several interconnected mechanisms: (1) a hierarchy of semantic processing layers from raw data to abstract hypotheses; (2) multiple specialized agents (extractor, analyst, hypothesizer, critic, synthesizer) operating at different abstraction levels; (3) a resource-based cost of error where agents that perform poorly lose compute, context depth, and priority through natural allocation by demonstrated utility; (4) a probabilistic world map storing not assertions of truth but confidence weights, verification history, and applicability conditions; (5) the human as a trusted interface with reality — a sensor providing authentic signals, not a judge of correctness; (6) bilateral trust calibration where the system signals when operating outside its verified competence zone; (7) verification as a scarce resource creating pressure toward compact testable predictions and experimental design. The proposal also discusses feasibility at the current technology level, key risks including self-contamination, Goodhart's Law effects, and suppression of exploratory agents, and recommends a minimal viable implementation path.