A Formal Multi-Layer Architecture of Personality for AGI Systems

This work introduces a formal, system-theoretical model of personality as a multi-layer, self-regulating architecture compatible with AGI frameworks. The model defines personality as a dynamic system S = (I, L, O, F) and represents it as a directed graph (G = V, E), enabling analysis of stability, perturbation, and adaptive behavior. Abstract This work introduces a formal, system-theoretical model of personality as a multi-layer, self-regulating architecture designed to be compatible with artificial general intelligence (AGI) frameworks. Rather than treating personality as a static set of traits, the model conceptualizes it as a dynamic regulatory system composed of hierarchically organized functional layers (L0–L9), spanning physiological reactivity, affective processing, cognitive control, identity construction, social interaction, and meta-reflective regulation. The architecture is formalized using a system representation and a directed graph structure, enabling the analysis of stability, perturbation response, failure cascades, and recovery mechanisms. The model establishes a structural bridge between classical personality theory and computational architectures, with direct implications for AGI design, alignment, and adaptive behavior modeling. 1. Conceptual Foundation This work provides a formal bridge between personality theory and AGI system design. Personality is defined not as a static collection of traits, but as a multi-layer, self-regulating system operating through recursive feedback between internal states and environmental inputs. The model integrates insights from psychological, system-theoretical, and computational frameworks into a unified architecture that is both analytically tractable and implementable in artificial systems. 2. System Definition The personality system is formally defined as: S = (I, L, O, F) where: I = input (environmental and internal signals) L = layered architecture (L0–L9) O = output (behavioral and representational states) F = feedback (recursive regulatory processes) The system evolves dynamically according to: State(t+1) = F(State(t), Input(t)) This formulation captures personality as a time-dependent adaptive system, rather than a fixed structure. 3. Layered Architecture (L0–L9) The model is composed of hierarchically organized functional layers: L0–L1: physiological and affective reactivity L2–L3: perception, memory, and associative processing L4–L5: cognitive control and executive regulation L6: identity construction and self-model L7: social interaction and role systems L8–L9: meta-reflection, self-regulation, and long-term adaptation Each layer performs distinct regulatory functions while remaining dynamically coupled through feedback loops. 4. Graph Representation The architecture is also represented as a directed weighted graph: G = (V, E) where: V = {L0, L1, ..., L9} are functional nodes E are directed, weighted edges representing causal and feedback relations This representation enables formal analysis of: system stability perturbation propagation failure cascades recovery dynamics 5. Dynamics: State, Stability, and Failure The model distinguishes between: State: momentary configuration of the system Trait: statistically stable attractor patterns across states System behavior emerges from transitions between states under input and feedback constraints. Critical dynamics include: perturbation response (external or internal disruption) failure cascades (layer-to-layer destabilization) recovery cascades (re-stabilization through feedback) Example condition: If lower-layer stability (L0–L2) falls below threshold → cascade activation across higher layers. 6. AGI Mapping Each layer corresponds to potential AGI functional modules: L0–L1 → sensorimotor and affective systems L2–L3 → perception and memory architectures L4–L5 → planning and control systems L6 → self-model / identity representation L7 → social cognition modules L8–L9 → meta-learning and self-modification This mapping enables the translation of personality structure into computational architectures. 7. Theoretical Contribution The model extends classical psychology by: replacing static trait models with dynamic system representations integrating state–trait duality into formal dynamics embedding personality into feedback-based architectures enabling direct mapping to artificial systems Unlike traditional models, it is: formally definable computationally interpretable scalable to AGI design contexts 8. Conclusion Personality is a multi-layer, self-regulating system that transforms inputs into behavior through recursive feedback processes across hierarchical functional layers. Its stability emerges from attractor-like trait structures, while its adaptability arises from dynamic state transitions under environmental and internal perturbations. The system is neither fully deterministic nor fully flexible, but operates as a constrained adaptive architecture. This model provides a unified framework linking human personality theory with AGI-compatible system design.