Endogenously-Constrained AI Toward Structurally Stable and Auditable Artificial Intelligence

Endogenously-Constrained AI – Toward Structurally Stable and Auditable Artificial Intelligence Description This work introduces Endogenously-Constrained Artificial Intelligence (ECA), a novel architectural framework designed to ensure structural stability, scientific reproducibility, and external auditability in advanced AI systems. ECA integrates cumulative memory, endogenous regulatory thresholds, and CBD regimes (Convergence–Bifurcation–Dissipation) to model internal dynamics as an emergent, self-regulated process rather than a purely reactive optimization loop. The framework formalizes how internal states, latent and activated divergences, and saturation thresholds govern observable behavior over time. A unified functional expression links internal psychodynamic structure with contextual opportunities, enabling the identification of stability, critical transitions, and dissipation phases without requiring access to source code or internal parameters. This approach resolves the paradox of apparent stability masking structural fragility by making internal regimes externally observable and scientifically traceable. The ECA+CBD architecture is applicable to heterogeneous, multimodal, and agentic AI systems, particularly in critical domains where robustness, governance, and certification are required. It establishes a general, architecture-agnostic foundation for auditable, governable, and structurally safe artificial intelligence.