# V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models

arXiv: https://arxiv.org/abs/2511.16668 · HTML: https://mlsanity.com/paper/2511.16668/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 26 of its 34 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 26 of 26)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [Movie Gen: A Cast of Media Foundation Models](https://mlsanity.com/paper/2410.13720/) (2024)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [CLEVRER: CoLlision Events for Video REpresentation and Reasoning](https://mlsanity.com/paper/1910.01442/) (2019)
- [CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models](https://mlsanity.com/paper/2503.22020/) (2025)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2309.15818/) (2023)
- [MLVU: Benchmarking Multi-task Long Video Understanding](https://mlsanity.com/paper/2406.04264/) (2024)
- [Seedance 1.0: Exploring the Boundaries of Video Generation Models](https://mlsanity.com/paper/2506.09113/) (2025)
- [VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2504.06958/) (2025)
- [Video models are zero-shot learners and reasoners](https://mlsanity.com/paper/2509.20328/) (2025)
- [CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning](https://mlsanity.com/paper/1910.04744/) (2019)
- [AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning](https://mlsanity.com/paper/2103.16002/) (2021)
- [VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning](https://mlsanity.com/paper/2505.12434/) (2025)
- [TopViewRS: Vision-Language Models as Top-View Spatial Reasoners](https://mlsanity.com/paper/2406.02537/) (2024)
- [VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information](https://mlsanity.com/paper/2412.00947/) (2024)
- [CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting](https://mlsanity.com/paper/2504.15485/) (2025)
- [Chain-of-Frames: Advancing Video Understanding in Multimodal LLMs via Frame-Aware Reasoning](https://mlsanity.com/paper/2506.00318/) (2025)
- [VChain: Chain-of-Visual-Thought for Reasoning in Video Generation](https://mlsanity.com/paper/2510.05094/) (2025)
- [VidHal: Benchmarking Temporal Hallucinations in Vision LLMs](https://mlsanity.com/paper/2411.16771/) (2024)
- [A Survey on Efficient Vision-Language Models](https://mlsanity.com/paper/2504.09724/) (2025)
- [ALLVB: All-in-One Long Video Understanding Benchmark](https://mlsanity.com/paper/2503.07298/) (2025)

## Built on by (most cited first, 22 of 22)

- [A Very Big Video Reasoning Suite](https://mlsanity.com/paper/2602.20159/) (2026)
- [DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models](https://mlsanity.com/paper/2512.24165/) (2025)
- [Demystifying Video Reasoning](https://mlsanity.com/paper/2603.16870/) (2026)
- [CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation](https://mlsanity.com/paper/2601.10061/) (2026)
- [Video Models Can Reason with Verifiable Rewards](https://mlsanity.com/paper/2605.15458/) (2026)
- [WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors](https://mlsanity.com/paper/2605.10434/) (2026)
- [VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning](https://mlsanity.com/paper/2608.26105/) (2026)
- [MME-CoF-Pro: Evaluating Reasoning Coherence in Video Generative Models with Text and Visual Hints](https://mlsanity.com/paper/2603.20194/) (2026)
- [VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization](https://mlsanity.com/paper/2606.02564/) (2026)
- [Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning](https://mlsanity.com/paper/2512.24952/) (2025)
- [From Perception to Action: An Interactive Benchmark for Vision Reasoning](https://mlsanity.com/paper/2602.21015/) (2026)
- [CollabVR: Collaborative Video Reasoning with Vision-Language and Video Generation Models](https://mlsanity.com/paper/2605.08735/) (2026)
- [OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics](https://mlsanity.com/paper/2606.09826/) (2026)
- [V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration](https://mlsanity.com/paper/2603.13089/) (2026)
- [On-Policy Adversarial Flow Distillation for Autoregressive Video Generation](https://mlsanity.com/paper/2605.26105/) (2026)
- [OpenCoF: Learning to Reason Through Video Generation](https://mlsanity.com/paper/2607.08763/) (2026)
- [VGI-Bench: Probing Visual Intelligence in Video Generation Models](https://mlsanity.com/paper/2608.19583/) (2026)
- [Hierarchical Denoising For Multi-Step Visual Reasoning](https://mlsanity.com/paper/2607.15278/) (2026)
- [Apple-π: Benchmarking Thinking with Video Towards Law-Grounded Physical Intelligence](https://mlsanity.com/paper/2607.16401/) (2026)
- [Thinking in Video: Can Video Generators Really Reason About the Real World?](https://mlsanity.com/paper/2607.17523/) (2026)
- [Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation](https://mlsanity.com/paper/2609.02864/) (2026)
- [From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models](https://mlsanity.com/paper/2609.11242/) (2026)

## Abstract (arXiv)

Recent progress in generative video models, such as Veo-3, has shown surprising zero-shot reasoning abilities, creating a growing need for systematic and reliable evaluation. We introduce V-ReasonBench, a benchmark designed to assess video reasoning across four key dimensions: structured problem-solving, spatial cognition, pattern-based inference, and physical dynamics. The benchmark is built from both synthetic and real-world image sequences and provides a diverse set of answer-verifiable tasks that are reproducible, scalable, and unambiguous. Evaluations of six state-of-the-art video models reveal clear dimension-wise differences, with strong variation in structured, spatial, pattern-based, and physical reasoning. We further compare video models with strong image models, analyze common hallucination behaviors, and study how video duration affects Chain-of-Frames reasoning. Overall, V-ReasonBench offers a unified and reproducible framework for measuring video reasoning and aims to support the development of models with more reliable, human-aligned reasoning skills.