Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
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Rolling Sink is a training-free cache adjustment technique that maintains visual consistency in autoregressive video diffusion models for ultra-long open-ended generation beyond training horizons.
DiffGap introduces adaptive alignment of denoising steps and temperature annealing in diffusion models for 3D molecule generation, reporting better docking scores and binding affinity than prior methods on CrossDocked2020.
citing papers explorer
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Mechanisms of Misgeneralization in Physical Sequence Modeling
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
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Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion
Rolling Sink is a training-free cache adjustment technique that maintains visual consistency in autoregressive video diffusion models for ultra-long open-ended generation beyond training horizons.
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Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation
DiffGap introduces adaptive alignment of denoising steps and temperature annealing in diffusion models for 3D molecule generation, reporting better docking scores and binding affinity than prior methods on CrossDocked2020.