Learning via Self-Consistency for Diffusion-based Video Reasoning

arXiv:2609.36826 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Consistency — 37× in this paper · explained in 3 episodes
• Self-consistency — 35× in this paper · explained in 2 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Abstention — 13× in this paper · explained in 1 episode
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Aggregation — 9× in this paper · explained in 1 episode
• Test-time Scaling — 9× in this paper · explained in 2 episodes
• Decode — 8× in this paper · explained in 2 episodes
• Verifier — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Distillation — 6× in this paper · explained in 2 episodes
• Rejection Fine-Tuning — 6× in this paper
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Frozen model — 3× in this paper · explained in 3 episodes
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Verifiable Rewards — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Flow Matching — 2× in this paper · explained in 8 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Test-Time Scaling (TTS) — 1× in this paper · explained in 2 episodes

Sources

• Proprio: Latent Self-Scoring and Inference-Time Refinement for Physically Plausible Video Generation
• Wan-R1: Verifiable-Reinforcement Learning for Video Reasoning
• Video Models Reason Early: Exploiting Plan Commitment for Maze Solving
• Video Generation Models are General-Purpose Vision Learners
• Demystifying Video Reasoning
• Video models are zero-shot learners and reasoners
• Reasoning via Video: The First Evaluation of Video Models' Reasoning Abilities through Maze-Solving Tasks
• Video Models Can Reason with Verifiable Rewards

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