From Probabilistic Engines to Complex Systems: A Comparative Analysis of Emergence and Cognition in Modern AI

Mirage or Mechanism? Unifying the AI Emergence Debate through Complexity Science and Attractor Dynamics

This comparative analysis challenges the reductionist view of Large Language Models as simple probabilistic engines by framing them through the rigorous lens of Complexity Science and non-linear dynamics. Addressing the critical controversy of whether emergent abilities are a "metric-driven mirage" or a genuine "discontinuous leap," the paper synthesizes these opposing arguments into a unified theory where discontinuous metrics act as amplifiers for observing genuine internal phase transitions. By contrasting the metaphorical "cognitive architectures" of current agentic systems with the mechanistic reality of "attractor networks" and chaotic dynamics, this work offers a bottom-up, physics-based explanation for how memory and reasoning self-organize as stable patterns within high-dimensional state spaces. This framework provides a crucial roadmap for moving beyond the simulation of cognition to scientifically modeling the internal mechanisms of emergent intelligence.