Abstract

Individual artificial intelligence systems face an inherent trade-off between plasticity and stability under resource constraints. I propose that general intelligence emerges from networks of specialized agents applying a structured reasoning cycle to answer four fundamental questions. Agents ground abstract patterns through affective valence embeddings and coordinate via a shared database of credibility-weighted knowledge packages. I formalize a five-stage reasoning engine (Salience Detection → Hypothesis Generation → Experimentation → Structural Correspondence → Generalization) where agents at different stages specialize in different questions, enabling zero-shot cross-domain transfer. Using ARC-AGI task "as66" as demonstration, I show 276 generations of evolutionary learning where complementary specialization yields a current maximum of Level 4 performance across agents [20]. This framework provides testable predictions for performance scaling, transfer capability, and behavioral signatures of reasoning integration.

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