Core thesis: Intelligence is not an engineering phenomenon but a physical phenomenon—specifically, a stochastic field far from thermal equilibrium. This paper presents Unified Intelligo-Dynamics (UID), a three-tier physical theoretical framework for intelligent architectures: classical Intelligo-Dynamics (CID), quantum Intelligo-Dynamics (QID), and field Intelligo-Dynamics (FID). Starting from three first-principles axioms of open-system physics—Hamiltonian reversibility, the Gibbs statistical postulate, and slow-fast time-scale separation—UID rigorously derives the generalized Langevin equation as the governing law of intelligent system evolution via Mori-Zwanzig projection. The framework is then extended in two directions: at the quantum level, by introducing zero-point fluctuations, Berry geometric phases, and Lindblad dissipation channels, yielding the QID master equation; at the geometric level, by paralleling the Fisher information metric with the Einstein tensor, yielding the FID field equations. We rigorously prove: the predictive capacity of intelligent systems (measured by conditional mutual information) necessarily requires their internal dynamics to break detailed balance—this is the non-equilibrium physical essence of intelligence, and the precise meaning of the paper's title "Intelligence Is a Non-Equilibrium Field". Precise characterization of "Attention Is Not All You Need": We demonstrate that mainstream deep learning architectures—Transformer, Mamba, diffusion models, JEPA, reasoning-enhanced models (DeepSeek-R1, o1-o3), and sparse routing architectures (SubQ/SSA)—are all special cases of the CID master equation under different limits (zero curl, white noise, single heat bath, within softmax-attention interface). Vaswani et al.'s 2017 "Attention Is All You Need" revealed the associative-memory term of CID; but the CID master equation also contains three critical physical terms that Transformer discards—curl v(φ), colored damping ∫γ, and colored noise ξ. The absence of these three terms is precisely the algorithmic root of current AI consuming approximately one million times more energy than human brains. The Attention quadratic complexity lower bound proven by Alman-Song (2023) and Gupta et al. (2025) further indicates: any optimization within the softmax-attention framework cannot break this complexity wall; true breakthroughs must come from architectural-level physical reconstruction—precisely the direction UID argues for. Falsifiable predictions: On this basis, we propose a falsifiable engineering target of approximately tenfold parameter efficiency, and provide three sets of critical universality-class predictions that have been independently confirmed in biological brains: avalanche-size exponent τ ≈ 1.5 (Beggs future fusion directions are worth exploring. All references in this paper provide clickable DOIs or open-access links, and all quantitative claims are explicitly tagged with their empirical-evidence grade (A verified / B theoretically estimated / C to-be-verified / D philosophical conjecture). The companion code repository (github.com/gwailee/uid) provides an engineering reference implementation of CID and a falsifiable validation suite; all core predictions can be reproduced within hours on a single GPU.
Li et al. (Mon,) studied this question.