Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling

• URL: http://arxiv.org/abs/2607.09892v1
• Date: Fri, 10 Jul 2026 18:32:27 GMT
• Title: Next-Dense-Stride Prediction for Multimodal Autoregressive Visual Modeling
• Abstract summary: We introduce DenseAR, a new generative paradigm that reformulates autoregressive image generation as coarse-to-fine next-dense-stride prediction.<n>Our key insight is that traversing a single-scale latent grid with progressively denser strides naturally captures the transition from global structure to fine detail.
• Score: 22.020464703520148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce DenseAR, a new generative paradigm that reformulates autoregressive image generation as coarse-to-fine next-dense-stride prediction using a compact single-scale tokenizer. Our key insight is that traversing a single-scale latent grid with progressively denser strides naturally captures the transition from global structure to fine detail. This addresses two limitations of existing autoregressive models at once: the slow inference of raster-order autoregression, which DenseAR avoids by predicting multiple tokens in parallel, and the heavy cost of multi-scale approaches, which need long, multi-resolution token sequences to achieve coarse-to-fine prediction. Building on our efficient framework and the flexibility of autoregressive modeling, we further extend DenseAR to a unified model that handles multiple modalities and imaging tasks within a single backbone. We validate DenseAR on both medical and natural images. On multi-contrast brain MRI, a single DenseAR model unifies cross-modal translation, modality-conditioned generation, and tumor segmentation, while remaining competitive with task-specific methods. On ImageNet, DenseAR improves class-conditional generation quality (FID and IS) over both a single-grid baseline without stride ordering and a multi-scale tokenizer-based baseline.

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