Hierarchical Denoising For Multi-Step Visual Reasoning
Video models are evolving into vision foundation models, yet they still lack human-like multi-step reasoning. Streaming autoregressive diffusion models are efficient but limited in reasoning, while bidirectional diffusion enables global revision with high inference costs due to dense frame-level denoising. Both paradigms struggle to achieve logical consistency and low-latency streaming for complex reasoning tasks. We propose HDR (Hierarchical Denoising for Visual Reasoning), a unified framework that integrates hierarchical latents into causal video generation for multi-step reasoning. HDR orga
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- FuzzyOverlapping authors or contributors · 62%ultralytics/ultralytics →
“Shared author/contributor keys: han”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzyOverlapping authors or contributors · 62%janhq/jan →
“Shared author/contributor keys: han”
- FuzzySimilar title/name (fuzzy) · 59%rasbt/reasoning-from-scratch →
“Fuzzy title match (0.73): “Hierarchical Denoising For Multi-Step Visual Reasoning” ≈ “rasbt/reasoning-from-scratch””
- LinkedLinked via arxiv author · 85%Zezhong Qian →
“Hierarchical Denoising For Multi-Step Visual Reasoning”
- LinkedLinked via arxiv author · 85%Xiaowei Chi →
“Hierarchical Denoising For Multi-Step Visual Reasoning”
- LinkedLinked via arxiv author · 85%Chak-Wing Mak →
“Hierarchical Denoising For Multi-Step Visual Reasoning”