{"ID":2884846,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06477","arxiv_id":"2508.06477","title":"Intuition emerges in Maximum Caliber models at criticality","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.06477","url_pdf":"https://arxiv.org/pdf/2508.06477v2","authors":"[\"Lluís Arola-Fernández\"]","published":"2025-08-08T17:27:41Z","proceeding":"physics.soc-ph","tasks":"[\"physics.soc-ph\",\"cond-mat.dis-nn\",\"cond-mat.stat-mech\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}