Self-Transcendence: Achieving AGI via Chaotic Dynamics and Thermodynamic Attractors

Abstract

This position paper addresses the "Architectural Trap", a fundamental constraint in deep learning where systems effectively optimize parameters within a fixed architecture but cannot discover radically superior structural designs. This trap constitutes a barrier within a non-continuous, high-dimensional search space that gradient-based and conventional search methods are ill-equipped to cross. We propose "Self-Transcendence" (ST) as a novel framework for artificial intelligence, defined by an agent's capacity to 1) model its own cognitive limitations, 2) evaluate its architecture via this internal model, and 3) execute discontinuous, non-local architectural modifications through controlled, chaotic dynamics. The central hypothesis is that this mechanism emerges from a synthesis of two physical principles: a thermodynamic "pull" and a chaotic "push". The "pull" is identified as the thermodynamic imperative for open, dissipative systems to self-organize into more stable, energy-rich "attractor states". These states are superior at modeling their environment to minimize "surprise", a concept formalized in the Free-Energy Principle [10]. This connects to active research in dissipation-driven adaptation [8] and its modern application in thermodynamic AI. The "push" is the mechanism of deterministic chaos, which provides a structured, efficient means of exploring the vast, discontinuous architectural phase space [37]. This aligns with recent findings that optimal deep learning performance often occurs at the edge of chaos [21]. Guided by principles of Self-Organized Criticality [24, 3], the system makes a "thermodynamic bet": it risks short-term instability (the chaotic push) to jump a local energy barrier, allowing it to "roll down" to the more powerful, thermodynamically-favored attractor (the pull). We argue this framework provides a viable mechanism for true Recursive Self-Improvement (RSI), moving beyond mere parameter-tuning to enable the structural, paradigm-shifting leaps required for Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI).