arXiv AI
ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction
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Training-Free Predictive Spatio-Temporal Token Pruning for Few-Step Autoregressive Video Diffusion
arXiv:2611.07551v1 Announce Type: new Abstract: Few-step autoregressive (AR) video diffusion models generate videos chunk by chunk and have made efficient video generation practical. However, further...
arXiv Computer Vision
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RESUME: Recurrent State Updates from Motion and Residual Signals for Efficient Video Language Modeling
arXiv:2609.39563v1 Announce Type: new Abstract: Existing video language models encode sampled RGB frames independently, so a long video must either exhaust the token budget or drop the changes betwee...
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MT-WAM: Reorienting the One-Pass Predictive Representation Toward Action Generation
MT‑WAM enhances the Fast‑WAM framework by adding complementary supervision for future 2‑D point trajectories and visual features while keeping the original training objectives. A lightweight dual‑stream branch and structured attention mask isolate motion‑specific processing, and motion‑stream tokens provide additional dynamics cues to the action expert. During inference, MT‑WAM skips future‑video prediction, using cached video and motion information to achieve higher success rates on LIBERO, LIBERO‑Plus, RoboTwin 2.0 Clean2Rand, and several real‑world tasks.
arXiv Machine Learning
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Maglev: Sliding Recurrent Memory
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arXiv AI
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V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents
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arXiv Computer Vision
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FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation
FlashAR is a lightweight post‑training adaptation framework that converts a pre‑trained raster‑scan autoregressive image model into a highly parallel generator using two‑way next‑token prediction. It preserves the original training objective by keeping the horizontal head for row‑wise prediction and adding a lightweight vertical head for column‑wise prediction, with a learnable fusion gate to combine the two predictions. A two‑stage adaptation pipeline—first initializing the vertical head from the pre‑trained model and then jointly fine‑tuning—yields up to a 22.9× speedup for 512×512 image generation while using only 0.05% of the original training data.