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A training-free change detection method achieves near-optimal performance by integrating diffusion models to precisely estimate Hyv盲rinen scores, slashing detection delays by over 90% in simulations.
In-context learning is not limited to discriminative prediction: frozen transformers run full iterative diffusion and energy-based sampling within a single forward pass.
Even with Byzantine attackers sending arbitrary adversarial messages, this new gradient tracking method provably converges linearly to a small error neighborhood.
