# Video Models Start to Solve Chess, Maze, Sudoku, Mental Rotation, and Raven' Matrices

arXiv: https://arxiv.org/abs/2512.05969 · HTML: https://mlsanity.com/paper/2512.05969/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 53 references that are in the map.
- First posted on arXiv 2 Nov 2025.

## Builds on (most cited first, 30 of 31)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.09712/) (2022)
- [Video models are zero-shot learners and reasoners](https://mlsanity.com/paper/2509.20328/) (2025)
- [VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation](https://mlsanity.com/paper/2406.15252/) (2024)
- [Do generative video models understand physical principles?](https://mlsanity.com/paper/2501.09038/) (2025)
- [WorldModelBench: Judging Video Generation Models As World Models](https://mlsanity.com/paper/2502.20694/) (2025)
- [GenAI Arena: An Open Evaluation Platform for Generative Models](https://mlsanity.com/paper/2406.04485/) (2024)
- [ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation](https://mlsanity.com/paper/2406.18522/) (2024)
- [Video-Bench: Human-Aligned Video Generation Benchmark](https://mlsanity.com/paper/2504.04907/) (2025)
- [Core Knowledge Deficits in Multi-Modal Language Models](https://mlsanity.com/paper/2410.10855/) (2024)
- [Language Models as Models of Language](https://mlsanity.com/paper/2408.07144/) (2024)
- [How Do Transformers Learn Variable Binding in Symbolic Programs?](https://mlsanity.com/paper/2505.20896/) (2025)
- [A Survey of AI-Generated Video Evaluation](https://mlsanity.com/paper/2410.19884/) (2024)
- [Vision Language Models See What You Want but not What You See](https://mlsanity.com/paper/2410.00324/) (2024)
- [Probing Mechanical Reasoning in Large Vision Language Models](https://mlsanity.com/paper/2410.00318/) (2024)
- [From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens](https://mlsanity.com/paper/2510.02292/) (2025)
- [Probing Perceptual Constancy in Large Vision-Language Models](https://mlsanity.com/paper/2502.10273/) (2025)
- [The Philosophical Foundations of Growing AI Like A Child](https://mlsanity.com/paper/2502.10742/) (2025)
- [Recognizing Artificial Minds: A Philosophical Defense of AI Cognition](https://mlsanity.com/paper/2504.13988/) (2025)
- [The Mechanistic Emergence of Symbol Grounding in Language Models](https://mlsanity.com/paper/2510.13796/) (2025)
- [Vision Language Models Know Law of Conservation without Understanding More-or-Less](https://mlsanity.com/paper/2410.00332/) (2024)
- [Rethinking the Simulation vs. Rendering Dichotomy: No Free Lunch in Spatial World Modelling](https://mlsanity.com/paper/2510.20835/) (2025)
- [Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind](https://mlsanity.com/paper/2505.03770/) (2025)
- [Increasing Computation Resolves Conflicts in Vision Language Models](https://mlsanity.com/paper/2505.18969/) (2025)

## Built on by (most cited first, 4 of 4)

- [CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation](https://mlsanity.com/paper/2601.10061/) (2026)
- [Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning](https://mlsanity.com/paper/2512.24952/) (2025)
- [V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration](https://mlsanity.com/paper/2603.13089/) (2026)
- [Thinking in Video: Can Video Generators Really Reason About the Real World?](https://mlsanity.com/paper/2607.17523/) (2026)

## Abstract (arXiv)

We show that video generation models could reason now. Testing on tasks such as chess, maze, Sudoku, mental rotation, and Raven's Matrices, leading models such as Sora-2 achieve sixty percent success rates. We establish a robust experimental paradigm centered on the "Task Pair" design. We build a code framework, with 39 models available already, that supports this paradigm and allows for easy scaling - users can add models and tasks efficiently. We show our automated evaluation strongly correlates with human judgment, and therefore this paradigm is highly scalable. We see an opportunity, given the availability of our paradigm, to do reinforcement learning for improving reasoning in video models. You could checkout all of our raw hrefhttps://grow-ai-like-a-child.com/video-reason/results and our hrefhttps://github.com/hokindeng/VMEvalKitVMEvalKit codebase.