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Diffusion models can learn lattice field theories without ever seeing real data, matching HMC performance.
A novel two-stream architecture disentangles temporal context from spatial refinement in EEG source imaging, achieving superior localization accuracy without compromising inference speed.
Generative samplers for lattice field theory historically fail near the continuum limit due to non-local link correlations, but decomposing Bianchi constraints into four-variable local solves unlocks direct, scalable plaquette-space flow architectures.
A new attribution method reveals the full spectrum of information flow in ETGNNs, enhancing explainability beyond traditional event-related embeddings.
Exponential complexity in GNN explainability is no longer a barrier: this work achieves linear-time subgraph attribution via message passing.
