Author

Olga Russakovsky

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Preprint Oct 2026

In-Context Learning Can Help Vision-Language Models Overcome Training Priors

Vision-language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence when input images violate those regularities. It is challenging to disambiguate these prior-induced errors from errors caused by the model missing the underlying visual ca...

Kun Wang, Xin-Di Wu, Sanghyuk Chun et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Depth as Time in One-Step Generative Models

The recent wave of one-step generative models, which compress the multi-step trajectory of diffusion via either distillation or learned flow maps, has reached an inflection point where they can generate high-quality images. Here, we ask a natural question that follows from these advances: what happens to the denoising...

Arnold Caleb Asiimwe, William Yang, Sanghyuk Chun et al. · 0 citations

Preprint Aug 2026

Addressable Memory for Video World Models

This work proposes WorldTrace, a training-free memory framework for long-horizon visual persistence that keeps compressed memory addressable by assigning each summary slot a distinct, in-distribution virtual position.

Xindi Wu, Sven Elflein, James Lucas et al. · 1 citation