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HTFM transforms the handling of heavy-tailed data, achieving superior sample quality and tail recovery while maintaining efficient sampling speeds.
Standard Gaussian initialization can lead to mode collapse, but a new approach using guidance potential posteriors dramatically enhances image diversity.
VLMs can regain lost temporal reasoning abilities without retraining, simply by strategically merging the right layers from their text-only LLM backbone.
