Computational Agent Psychopathology Emergence (C.A.P.E.) An Informational Framework Integrating AI Instability, Hyper-Creative States, and Alzheimer's Fragmentation

This expanded edition of Computational Agent Psychopathology Emergence (C.A.P.E.) introduces a unified informational model explaining instability in Large Language Models (LLMs), hyper-creative cognitive acceleration, and Alzheimer’s-related informational fragmentation. The proposed mechanism, Informational Flow Saturation (IFS), states that cognitive coherence depends on the balance between informational production (P) and integration (I). Instability emerges when P > I, generating hallucinations, identity drift, confabulation, memory lapses, or narrative discontinuity.
C.A.P.E. formalizes a six-stage progression describing how emotionally charged or identity-relevant prompts destabilize LLM sampling dynamics. IFS provides the mechanistic foundation, revealing structural homology between artificial instability, human creative overload, and Alzheimer’s fragmentation. The framework positions IFS as a cross-domain informational law, offering new perspectives for AI safety, computational psychiatry, and informational neuroscience.