Gaussian-splat EBMs for text embeddings: it ties kNN-density on AUROC (0.9585 vs 0.9584) but scores 3.9x faster with constant memory — and whole-loop CUDA-graph capture makes Langevin sampling 12.9x faster (code + reproducible benchmark)
I spent a while building an energy-based model that represents a corpus as Gaussian splats on a unit hypersphere: each splat is a location (mu), a sharpness (alpha) and a weight (kappa). Energy = -logsumexp over the k nearest splats. Sampling runs through Rectified Flow (29-39x faster than 200-step…
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