A Multi Layer Cognitive Architecture for Stable, Coherent, Agentic AI Systems: RA/PMT + LSA+SSM

This work presents a multi‑layer cognitive architecture designed to stabilize modern agentic AI systems and prevent structural failures such as representational drift, discontinuous goals, identity fragmentation, and misaligned action selection. Current AI models collapse heterogeneous information into uniform vector spaces and operate without constitutional constraints, leading to unstable internal states and unreliable behavior. The proposed architecture integrates two complementary frameworks: RA/PMT (Regional Arenas / Perception Mechanism Theory), which distinguishes constitutional intent from execution‑level intentions, and LSA+SSM (Layered Structural Architecture + Structural Selection Mechanism), which provides global constraints, multimodal integration, pattern‑level structure, and a governance mechanism for selecting operative internal states under explicit rules. Together, these frameworks form a model‑agnostic constitutional layer that preserves continuity, enforces domain boundaries, and aligns internal and external states. This work advocates a transition from model‑centric AI to architecture‑centric AI, establishing the structural foundations required for stable, coherent, and safe artificial cognition.