揭示了Transformer模型中慢速模式的红外组织规律及其对记忆与认知的关键影响。

Large language models exhibit remarkable emergent behaviors, yet the physical mechanism governing their collective dynamics remains poorly understood. Cognitive Field Theory predicts that learning reorganizes the collective relaxation spectrum, thereby modifying memory self-energy, long-memory dynamics, and collective susceptibility through the infrared organization of slow relaxation modes. Here we test this framework directly in Transformer dynamics. Using publicly available Pythia language models, we extract relaxation spectra from layer Jacobians throughout training, prompt ensembles, network depth, and model scale, allowing the collective observables of Cognitive Field Theory to be measured quantitatively. The measurements reveal pronounced infrared reorganization of the relaxation spectrum. Learning substantially redistributes spectral weight while preserving a nearly flat but weakly infrared-enhanced time-scale density of states, \( ρ(λ)\simλ^β, \qquad β\simeq-0.1, \) with a corresponding memory kernel exhibiting robust long-memory scaling close to \( K(t)\sim\frac{1}{t}. \) The collective observables further reveal a critical formation process: the memory self-energy reaches a transient maximum during early training before relaxing toward a metastable near-critical regime. Prompt-resolved and token-subspace measurements show that distinct local Jacobians recover a common macroscopic TDOS with shared infrared scaling, consistent with infrared fixed-point organization under coarse graining. The reproducibility of this infrared organization across training, prompt ensembles, network depth, and Transformer model scales supports infrared slow-mode organization as a robust collective principle of Transformer dynamics, providing a quantitative experimental realization of the collective observables predicted by Cognitive Field Theory.