The unprecedented predictive success of deep generative models in complex many-body systems, such as AlphaFold 3, raises an epistemological question: do these networks merely memorize data distributions via high-dimensional interpolation, or do they autonomously deduce the underlying physical laws? To address this, we introduce a framework to extract the implicit physical interactions learned by generative models. Using the exact equivalence between the zero-noise limit of a diffusion score field and the thermodynamic restoring force, we directly compare the internal interaction structure of a trained neural network with the physical Hamiltonian. Applying this framework to a sequence-dependent, frustrated 1D O(3) spin glass, we probe the latent representations of an O(3)-equivariant attention architecture trained solely on thermal equilibrium snapshots. Without imposing a locality cutoff or configuration independence, the dense scalar matrix in our architecture develops locality and nearly configuration-independent coefficients that recover the microscopic interactions of the spin glass with Pearson correlation 0.993 on 2,000 unseen sequences. This provides quantitative, falsifiable evidence for the emergence of the Hamiltonian in a deep generative model. Similarly, in an explicit-water phenol experiment, the network is trained only on coordinate data. With sufficient expressive capacity, a molecular-graph-guided Pairformer recovers the dominant bond, angle, and proper-torsion potential-of-mean-force (PMF) sectors. A sector-preserving projection of configuration-dependent coefficients onto constants yields R_(rm const)²=0.9669 on unseen molecular dynamics (MD), providing molecular-scale evidence for partial emergence of the Hamiltonian.