# VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding

arXiv: https://arxiv.org/abs/2505.01481 · HTML: https://mlsanity.com/paper/2505.01481/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 63 of its 90 references that are in the map.
- First posted on arXiv 2 May 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 30 of 63)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2503.06749/) (2025)
- [HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models](https://mlsanity.com/paper/2310.14566/) (2023)
- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [MovieChat: From Dense Token to Sparse Memory for Long Video Understanding](https://mlsanity.com/paper/2307.16449/) (2023)
- [Video-R1: Reinforcing Video Reasoning in MLLMs](https://mlsanity.com/paper/2503.21776/) (2025)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models](https://mlsanity.com/paper/2309.15103/) (2023)
- [VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness](https://mlsanity.com/paper/2503.21755/) (2025)
- [R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization](https://mlsanity.com/paper/2503.12937/) (2025)

## Built on by (most cited first, 30 of 32)

- [R-Zero: Self-Evolving Reasoning LLM from Zero Data](https://mlsanity.com/paper/2508.05004/) (2025)
- [Self-Rewarding Vision-Language Model via Reasoning Decomposition](https://mlsanity.com/paper/2508.19652/) (2025)
- [VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation](https://mlsanity.com/paper/2601.23286/) (2026)
- [MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data](https://mlsanity.com/paper/2603.09206/) (2026)
- [Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding](https://mlsanity.com/paper/2509.11866/) (2025)
- [First Frame Is the Place to Go for Video Content Customization](https://mlsanity.com/paper/2511.15700/) (2025)
- [Semantically-Aware Rewards for Open-Ended R1 Training in Free-Form Generation](https://mlsanity.com/paper/2506.15068/) (2025)
- [MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models](https://mlsanity.com/paper/2511.18373/) (2025)
- [Know-Show: Benchmarking Video-Language Models on Spatio-Temporal Grounded Reasoning](https://mlsanity.com/paper/2512.05513/) (2025)
- [Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation](https://mlsanity.com/paper/2512.24271/) (2025)
- [CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models](https://mlsanity.com/paper/2601.04778/) (2026)
- [MTAVG-Bench 2.0: Diagnosing Failure Modes of Cinematic Expressiveness in Multi-Talker Audio-Video Generation](https://mlsanity.com/paper/2605.28035/) (2026)
- [Compositional Coordination for Multi-Robot Teams with Large Language Models](https://mlsanity.com/paper/2507.16068/) (2025)
- [PIP-LLM: Integrating PDDL-Integer Programming with LLMs for Coordinating Multi-Robot Teams Using Natural Language](https://mlsanity.com/paper/2510.22784/) (2025)
- [Distilling Counterfactual Reasoning from Language to Vision: Causal Graph Guided Post-Training for Video Understanding](https://mlsanity.com/paper/2511.19923/) (2025)
- [Multimodal Language Models Cannot Spot Spatial Inconsistencies](https://mlsanity.com/paper/2604.00799/) (2026)
- [Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models](https://mlsanity.com/paper/2601.22574/) (2026)
- [Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models](https://mlsanity.com/paper/2602.11244/) (2026)
- [Distorted or Fabricated? A Survey on Hallucination in Video LLMs](https://mlsanity.com/paper/2604.12944/) (2026)
- [Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models](https://mlsanity.com/paper/2604.17873/) (2026)
- [Video-ToC: Video Tree-of-Cue Reasoning](https://mlsanity.com/paper/2604.20473/) (2026)
- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)
- [VERHallu: Evaluating and Mitigating Event Relation Hallucination in Video Large Language Models](https://mlsanity.com/paper/2601.10010/) (2026)
- [Follow the Clues, Frame the Truth: Hybrid-evidential Deductive Reasoning in Open-Vocabulary Multimodal Emotion Recognition](https://mlsanity.com/paper/2603.16463/) (2026)
- [Moir'e Video Authentication: A Physical Signature Against AI Video Generation](https://mlsanity.com/paper/2604.01654/) (2026)
- [Relaxing Anchor-Frame Dominance for Mitigating Hallucinations in Video Large Language Models](https://mlsanity.com/paper/2604.12582/) (2026)
- [From Priors to Perception: Grounding Video-LLMs in Physical Reality](https://mlsanity.com/paper/2605.04515/) (2026)
- [VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis](https://mlsanity.com/paper/2605.22570/) (2026)
- [Pop-Up Distractions Reveal Bag-of-Events Behavior in Video Large Language Models](https://mlsanity.com/paper/2605.27101/) (2026)
- [MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models](https://mlsanity.com/paper/2606.11792/) (2026)

## Abstract (arXiv)

Vision-Language Models (VLMs) have achieved strong results in video understanding, yet a key question remains: do they truly comprehend visual content or only learn shallow correlations between vision and language? Real visual understanding, especially of physics and common sense, is essential for AI systems that interact with the physical world. Current evaluations mostly use real-world videos similar to training data, so high benchmark scores may not reflect real reasoning ability. To address this, we propose negative-control tests using videos that depict physically impossible or logically inconsistent events. We introduce VideoHallu, a synthetic dataset of physics- and commonsense-violating scenes generated with Veo2, Sora, and Kling. It includes expert-annotated question-answer pairs across four categories of violations. Tests of leading VLMs (Qwen-2.5-VL, Video-R1, VideoChat-R1) show that, despite strong results on benchmarks such as MVBench and MMVU, they often miss these violations, exposing gaps in visual reasoning. Reinforcement learning fine-tuning on VideoHallu improves recognition of such violations without reducing standard benchmark performance. Our data is available at https://github.com/zli12321/VideoHallu.git.