{"ID":2837589,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19156","arxiv_id":"2511.19156","title":"Information Physics of Intelligence: Unifying Logical Depth and Entropy under Thermodynamic Constraints","abstract":"The rapid scaling of artificial intelligence models has revealed a fundamental tension between model capacity (storage) and inference efficiency (computation). While classical information theory focuses on transmission and storage limits, it lacks a unified physical framework to quantify the thermodynamic costs of generating information from compressed laws versus retrieving it from memory. In this paper, we propose a theoretical framework that treats information processing as an enabling mapping from ontological states to carrier states. We introduce a novel metric, Derivation Entropy, which quantifies the effective work required to compute a target state from a given logical depth. By analyzing the interplay between Shannon entropy (storage) and computational complexity (time/energy), we demonstrate the existence of a critical phase transition point. Below this threshold, memory retrieval is thermodynamically favorable; above it, generative computation becomes the optimal strategy. This \"Energy-Time-Space\" conservation law provides a physical explanation for the efficiency of generative models and offers a rigorous mathematical bound for designing next-generation, energy-efficient AI architectures. Our findings suggest that the minimization of Derivation Entropy is a governing principle for the evolution of both biological and artificial intelligence.","short_abstract":"The rapid scaling of artificial intelligence models has revealed a fundamental tension between model capacity (storage) and inference efficiency (computation). While classical information theory focuses on transmission and storage limits, it lacks a unified physical framework to quantify the thermodynamic costs of gene...","url_abs":"https://arxiv.org/abs/2511.19156","url_pdf":"https://arxiv.org/pdf/2511.19156v4","authors":"[\"Jianfeng Xu\",\"Zeyan Li\"]","published":"2025-11-24T14:24:08Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.AI\",\"cs.LO\"]","methods":"[]","has_code":false}