Intuition emerges in Maximum Caliber models at criticality
This addresses the fundamental question of whether AI models can achieve genuine insight, which is crucial for advancing AI capabilities beyond imitation, though it appears incremental as it builds on existing principles like Maximum Caliber.
The study tackled the problem of distinguishing between mere data parroting and genuine insight in large predictive models by discovering a primitive form of intuition that emerges as a metastable phase at criticality, balancing next-token prediction against future path-entropy, with models spontaneously discovering novel goal-directed strategies in a fragile window.
Whether large predictive models merely parrot their training data or produce genuine insight lacks a physical explanation. This work reports a primitive form of intuition that emerges as a metastable phase of learning that critically balances next-token prediction against future path-entropy. The intuition mechanism is discovered via mind-tuning, the minimal principle that imposes Maximum Caliber in predictive models with a control temperature-like parameter $λ$. Training on random walks in deterministic mazes reveals a rich phase diagram: imitation (low $λ$), rule-breaking hallucination (high $λ$), and a fragile in-between window exhibiting strong protocol-dependence (hysteresis) and multistability, where models spontaneously discover novel goal-directed strategies. These results are captured by an effective low-dimensional theory and frame intuition as an emergent property at the critical balance between memorizing what is and wondering what could be.