Abstract Current AI architectures, in their pursuit of output optimization, tend to converge toward a predictable "average." This paper proposes k-Foam Architecture, a novel computational model that applies the axiomatic structure of k-Foam Theory (originally a physical and cosmological framework) to multi-agent AI systems. Rather than eliminating errors, this architecture leverages "fluctuation (errors)" as a driving force for computation. By intentionally generating fluctuations and sifting out optimal solutions through rapid loss-cutting, the system sustains a state of "perpetual genius" and prevents the mediocritization caused by AI over-learning. Core Architecture (The 4 AI Personas + Judgment Layers) This model distributes the cognitive process across distinct functional layers: k1 Layer (Axioms): Absolute rules and judgment criteria without emotional bias. AI-1 (Path Creation): Generates destructive ideas ignoring existing context (Kamikaze Commander). AI-2 (Structuring): Extracts structure from AI-1's chaotic output and formalizes hypotheses (Architect). AI-3 (Convergence): Cross-references formulas against existing data without evaluation (Compiler). AI-4 (Skepticism): Actively seeks flaws and falsifies hypotheses that passed AI-3 (Red Team). k6 Layer (Decision): Final commit/discard judgment by the system architect or lightweight checksum AI. This autonomous distributed system continuously reinforces theory by spinning a closed-loop thinking process at high speed, effectively modeling the "non-fixation of pathways" characteristic of human genius. Related Work This architecture is the computational implementation of the theoretical physics framework "k-Foam Theory":https://zenodo.org/records/18886061
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