# Can World Simulators Reason? Gen-ViRe: A Generative Visual Reasoning Benchmark arXiv: https://arxiv.org/abs/2511.13853 ยท HTML: https://mlsanity.com/paper/2511.13853/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. - Builds on 27 of its 49 references that are in the map. - First posted on arXiv 17 Nov 2025. ## Placed in - LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/) ## Builds on (most cited first, 27 of 27) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025) - [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024) - [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025) - [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025) - [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023) - [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024) - [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022) - [Emerging Properties in Unified Multimodal Pretraining](https://mlsanity.com/paper/2505.14683/) (2025) - [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024) - [EvalCrafter: Benchmarking and Evaluating Large Video Generation Models](https://mlsanity.com/paper/2310.11440/) (2023) - [TAP-Vid: A Benchmark for Tracking Any Point in a Video](https://mlsanity.com/paper/2211.03726/) (2022) - [Seedance 1.0: Exploring the Boundaries of Video Generation Models](https://mlsanity.com/paper/2506.09113/) (2025) - [ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use](https://mlsanity.com/paper/2504.07981/) (2025) - [Video models are zero-shot learners and reasoners](https://mlsanity.com/paper/2509.20328/) (2025) - [T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation](https://mlsanity.com/paper/2407.14505/) (2024) - [FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation](https://mlsanity.com/paper/2311.01813/) (2023) - [Video-Bench: Human-Aligned Video Generation Benchmark](https://mlsanity.com/paper/2504.04907/) (2025) - [MindJourney: Test-Time Scaling with World Models for Spatial Reasoning](https://mlsanity.com/paper/2507.12508/) (2025) - [ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models](https://mlsanity.com/paper/2506.16211/) (2025) - [Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment](https://mlsanity.com/paper/2403.11956/) (2024) - [AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI](https://mlsanity.com/paper/2401.01651/) (2024) - [KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models](https://mlsanity.com/paper/2407.17773/) (2024) - [Embodied AI: From LLMs to World Models](https://mlsanity.com/paper/2509.20021/) (2025) - [Neural networks for abstraction and reasoning: Towards broad generalization in machines](https://mlsanity.com/paper/2402.03507/) (2024) - [EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks](https://mlsanity.com/paper/2503.11089/) (2025) - [GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines](https://mlsanity.com/paper/2508.06226/) (2025) ## Built on by (most cited first, 19 of 19) - [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) - [WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors](https://mlsanity.com/paper/2605.10434/) (2026) - [Video Understanding: From Geometry and Semantics to Unified Models](https://mlsanity.com/paper/2603.17840/) (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) - [CollabVR: Collaborative Video Reasoning with Vision-Language and Video Generation Models](https://mlsanity.com/paper/2605.08735/) (2026) - [Latent State Design for World Models under Sufficiency Constraints](https://mlsanity.com/paper/2605.01694/) (2026) - [YoCausal: How Far is Video Generation from World Model? A Causality Perspective](https://mlsanity.com/paper/2605.30346/) (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) - [PaintBench: Deterministic Evaluation of Precise Visual Editing](https://mlsanity.com/paper/2606.00188/) (2026) - [Thinking in Video: Can Video Generators Really Reason About the Real World?](https://mlsanity.com/paper/2607.17523/) (2026) - [Visual General Intelligence: A White Paper](https://mlsanity.com/paper/2608.25924/) (2026) - [From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models](https://mlsanity.com/paper/2609.11242/) (2026) - [Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model](https://mlsanity.com/paper/2609.18323/) (2026) - [Training Object Permanence in World Models](https://mlsanity.com/paper/2609.28654/) (2026) - [MindWorldBench: Evaluating Mental-State-to-Behavior Reasoning in Image-to-Video Generation](https://mlsanity.com/paper/2609.39147/) (2026) ## Abstract (arXiv) While Chain-of-Thought (CoT) prompting enables sophisticated symbolic reasoning in LLMs, it remains confined to discrete text and cannot simulate the continuous, physics-governed dynamics of the real world. Recent video generation models have emerged as potential world simulators through Chain-of-Frames (CoF) reasoning -- materializing thought as frame-by-frame visual sequences, with each frame representing a physically-grounded reasoning step. Despite compelling demonstrations, a challenge persists: existing benchmarks, focusing on fidelity or alignment, do not assess CoF reasoning and thus cannot measure core cognitive abilities in multi-step planning, algorithmic logic, or abstract pattern extrapolation. This evaluation void prevents systematic understanding of model capabilities and principled guidance for improvement. We introduce Gen-ViRe (Generative Visual Reasoning Benchmark), a framework grounded in cognitive science and real-world AI applications, which decomposes CoF reasoning into six cognitive dimensions -- from perceptual logic to abstract planning -- and 24 subtasks. Through multi-source data curation, minimal prompting protocols, and hybrid VLM-assisted evaluation with detailed criteria, Gen-ViRe delivers the first quantitative assessment of video models as reasoners. Our experiments on SOTA systems reveal substantial discrepancies between impressive visual quality and actual reasoning depth, establishing baselines and diagnostic tools to advance genuine world simulators.