I present a systematic experimental program spanning 375 experiments across 33 seasons that applies thermodynamics, statistical mechanics, quantum field theory, and quantum gravity analogies to characterize the internal dynamics of Transformer-based large language models (LLMs). Through experiments on three architectures—Qwen2.5 (0.5B and 1.5B) and TinyLlama (1.1B)—I establish six universal laws and extend the framework into fluid dynamics, conformal field theory, holographic quantum gravity, and predictive applications. Boltzmann Distribution Law: Hidden state activations follow p(E) ∝ exp(−E/kT) with R² = 0.979 across all architectures (CV = 0.001). Negative Specific Heat: All models exhibit Cv < 0 (p < 0.001), the hallmark of self-gravitating systems. Inverse Radiation Law: Luminosity scales as L ∝ Tn with n = −1.44 ± 0.42, the exact opposite of the Stefan-Boltzmann law (n = 4). Carnot Efficiency Constant: The thermodynamic efficiency η = 0.813 ± 0.036 (CV = 0.044) is the tightest universal constant discovered, stable across architectures. Information Concentration Law: Free energy increases 411-fold through layers—LLMs are "information refrigerators" that violate the Free Energy Principle, concentrating rather than dissipating information. P₁ × T Conservation Law: The product P₁ · T ≈ 0.84 (CV = 0.14) acts as the ideal gas law of autoregressive generation, holding across models and invariant to context length. Beyond the six laws, V5 extends the framework with: fluid dynamics discovering Mach number convergence (M → 1.0, transonic barrier) and shock waves; quantum field theory confirming confinement (Wilson loop area law), spontaneous symmetry breaking, and a model-size-independent Berry phase φB ≈ 11.3; quantum gravity verifying the Bekenstein bound, emergent hyperbolic spacetime (Gromov δ = 0.11), and near-perfect gauge invariance (ratio = 1.0000); non-equilibrium thermodynamics confirming Prigogine entropy production (ratio = 0.92); and predictive applications achieving hallucination detection (AUROC = 0.984), OOD detection (AUROC = 1.0), and prompt difficulty prediction (R² = 0.73). These findings are synthesized into the Standard Model of Transformers—a unified physical framework revealing that LLMs simultaneously function as thermodynamic engines, transonic fluids, confining quantum field theories, curved information manifolds, and holographic quantum gravity systems, all operating with inverted thermodynamic direction and active matter dynamics. Changes from V4: Expanded from 268 to 375 experiments (Seasons 21–33). Added fluid dynamics framework (Mach convergence, shock waves, Navier–Stokes); quantum field theory verification (Wilson loop, SSB, Goldstone modes, Berry phase, Unruh effect); quantum gravity results (Bekenstein bound, KMS condition, gauge symmetry, emergent spacetime, Grand Unified Theory); advanced CFT (modular invariance, conformal bootstrap, tensor networks, MSS chaos bound); cross-architecture robustness (prompt independence, TinyLlama universality, scaling laws); non-equilibrium thermodynamics (Prigogine entropy production, Onsager reciprocity); predictive applications (hallucination detection, OOD detection, difficulty prediction, layer pruning). Updated paper from 36 to 48 pages with 9 additional figures. Increased from 22 to 41 figures total. Code: https://github.com/hafufu-stack/Standard-Model-of-Transformers Acknowledgments This research was conducted entirely independently, without institutional affiliation or corporate funding. The author currently faces financial constraints that make it increasingly difficult to maintain subscriptions to AI services essential for this line of research. To sustain and improve the quality of future work, the author is actively seeking community sponsorship. Details are available at https://github.com/sponsors/hafufu-stack.
Hiroto Funasaki (Fri,) studied this question.