Toward a Stable and Aligned AGI Architecture

Vers une Architecture d’AGI Stable et Alignée.pdf

Download PDF • 1.32MB

Toward a Stable and Aligned AGI Architecture.pdf

Download PDF • 1.62MB

Architecture of a Stable AI Through Endogenous Constraints.pdf

Download PDF • 590KB

Beyond the AGI-ASI Myth.pdf

Download PDF • 613KB

Structural Auditability of Intelligent Systems.pdf

Download PDF • 624KB

This document proposes an architecture for stable and aligned AI, inspired by collective psychodynamics, by transposing concepts such as mimetic memory, emotional thresholds, and CBD (Convergence-Tilt-Dissipation) regimes to artificial systems.

It critiques current AI architectures for their reliance on exogenous feedback, leading to instability, reward hacking, and out-of-distribution fragility, and suggests cumulative internal constraints for endogenous stability.

The universal formula P(t) = A ⋅ ψ(S,R,V,M,D,C) × [O(t) ⋅ D(t)] models observable behavior, with variables such as M (cumulative memory), V (activation volume), and D (latent/active divergences) to limit dangerous states.

CBD regimes emerge naturally without explicit coding, via phases of dispersion, incubation, switching, persistence, and dissipation, illustrated by figures and minimal Python simulations in the appendix.

In conclusion, a safe AGI requires a restrictive internal structure (memory, thresholds, endogenous feedback) rather than external optimization, as a proof of concept for advanced agents.