Self-Emergence Agent Architecture:Behavior-Inertia HMM, Reflexive Metacognition,and Social-Contrastive Self-Modeling

The paper introduces the Self‑Emergence Agent Architecture (SEAA), a framework that combines a Hidden Markov Model for behavioral inertia, a reflexive metacognition loop that updates the HMM, and a social environment where agents compare behaviors. This closed loop enables agents to develop distinct, stable personalities and social structures without external prompts. Experiments with both a language‑model‑free prototype and hosted LLMs demonstrate spontaneous symmetry breaking and the emergence of consensus hubs and outliers.

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• llms
• agents