Hierarchical Denoising For Multi-Step Visual Reasoning

Video models are recently evolving into vision foundation models, but they still lack human-like, multi-step reasoning. Existing streaming autoregressive diffusion models are efficient but lack the reasoning ability, whereas bidirectional diffusion allows for global revision but incurs high inference cost due to the dense frames in fixed-sequence denoising. Consequently, both paradigms struggle to maintain logical consistency with low-latency streaming in complex reasoning tasks. Bridging this gap, we propose HDR (Hierarchical Denoising for Visual Reasoning), a unified framework for multi-step reasoning by integrating hierarchical latents into the causal video generation process. HDR organizes video latents into a tree-structured hierarchy to perform coarse-to-fine reasoning before streaming output. Coarse denoising layers maintain uncertain hypotheses for global planning, while finer denoising layers progressively refine them into concrete visual states. A sparse hierarchical attention pattern (SHAP) further reduces temporal attention cost. We construct a level-stratified multi-step video reasoning benchmark with out-of-distribution cases, covering six tasks: maze navigation, Tower of Hanoi, one-line drawing, sliding puzzle, Sokoban, and water pouring. Compared with the streaming autoregressive diffusion baseline, HDR improves overall success from 34.22 to 60.29 (76.2% relative gain) in multi-step reasoning accuracy, and improves average progress from 76.00 to 89.56, indicating more consistent intermediate reasoning trajectories. For deployment efficiency, HDR maintains low-latency streaming at 0.70s per latent, 54.2x faster than bidirectional diffusion during streaming. HDR also demonstrates strong data efficiency, retaining 82.9% of its full-data success score using only 2% of the training data, compared with 52.0% for bidirectional diffusion. Further experiments on real-world robots showcase the potential of HDR in physical interaction, providing a new paradigm for physical world modeling. Project demo page is available at https://hierarchical-diffusion-reasoning.github.io/.