Title: VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
Venue: NeurIPS 2025
Accepted: yes
Keywords: Vision-language Model

Video Understanding

Video Generation

Hallucination

Diffusion Models Monitoring
Abstract: Vision Language models (VLMs) have achieved remarkable success in video understanding tasks. Yet, a key question remains: Do they comprehend visual information or merely learn superficial mappings between visual and textual patterns? 
Understanding visual cues, particularly those related to physics and common sense, is crucial for AI systems interacting with the physical world. However, existing VLM evaluations primarily rely on positive-control tests using real-world videos that resemble training distributions. While VLMs perform well on such benchmarks, it is unclear whether they grasp underlying visual and contextual signals or simply exploit visual-language correlations. To fill this gap, we propose incorporating negative-control tests, i.e., videos depicting physically impossible or logically inconsistent scenarios, and evaluating whether models can recognize these violations. True visual understanding should evince comparable performance across both positive and negative tests. Since such content is rare in the real world, we introduce VideoHallu, a synthetic video dataset featuring physics- and commonsense-violating scenes generated using state-of-the-art tools such as Veo2, Sora, and Kling. The dataset includes expert-annotated question-answer pairs spanning four categories of physical and commonsense violations, designed to be straightforward for human reasoning. We evaluate several leading VLMs, including Qwen-2.5-VL, Video-R1, and VideoChat-R1. Despite their strong performance on real-world benchmarks (e.g., MVBench, MMVU), these models hallucinate or fail to detect physical or logical violations, revealing fundamental weaknesses in visual understanding. Finally, we explore reinforcement learning-based post-training on our negative dataset: fine-tuning improves performance on VideoHallu without degrading results on standard benchmarks, indicating enhanced visual reasoning in VLMs. Our data is available at https://github.com/zli12321/VideoHallu.git.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper evaluates counterintuitive, commonsense, and physics reasoning in videos. It introduces a benchmark of syntetically generated videos with commonsense and physical-law violations that are perceptually clear to humans but hallucinated by MLLMs with video understanding capabilities. It then evaluates state-of-the-art MLLMs on the proposed benchmark and proposes a GRPO-based reinforcement fine-tuning to cope with this problem.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:** The motivation of studying counterintuitive, commonsense, and physics reasoning in MLLMs with video understanding capabilities is interesting and well motivated. The paper evaluates several recent MLLMs, including less tested models trained with reinforcement learning or chain-of-thought supervised fine-tuning. Lastly, it proposes an approach to improve the poor performance of MLLMs on the proposed benchmark. \n\n**Weaknesses:** While the idea is interesting, several parts of the execution need, in my opinion,  improvement. Some parts of the data collection and the conducted evaluation are not clear, as detailed in the part dedicated to questions below. Moreover, although the final section of the paper introduces a methodology aimed at enhancing MLLM capabilities, it is evaluated on a single model with results that do not convincingly support the claims made.\nBoth main Tables 1 and 2 are complemented with Figures that, in my opinion, are useless. Figure 3 shows exactly the same numbers in Table 2, yet without the y-axis range, so it contains even less information. The same applies to Figure 5 and Table 3. I suggest removing these figures and using the space more cleverly. \n\n**Minor Weaknesses:** \n- *Black Box* in Table 2 may be an inappropriate name. I suggest changing it to closed-source or proprietary. \n- Unify LVLM and MLLM inconsistency: in the problem formulation in L87-99 LVLM is used while in the rest of the paper MLLM. \n- SOTA is missing the acronym, and sometimes it is written as SoTA (L133).\n\nQUESTIONS:\n**Q1:** The authors claim that: *after curriculum learning, MLLMs exhibit notable improvements in critical thinking and their ability to handle counterintuitive scenarios*, as stated based on Table 3. However, this conclusion is not sufficiently supported by the experimental evidence. \nSpecifically, the study evaluates RFT with curriculum learning on only a single model (Qwen2.5-VL-7B) and a single dataset (the authors' proposed benchmark).\nAs such, the **RTF results cannot be generalized to multiple MLLMs** without further validation. The authors should rephrase the claim or provide further experiments.  \n\n**Q2:** The authors claim that: *it is the quality and coverage of the data, not just the fine-tuning method, that drive gains* (L233-234). However, based on results in Table 3, it seems to me that Qwen2.5-VL-7B benefits from Curriculum Learning with composite datasets primarily in cases where **improvements were already evident under the Training Separately setting**. Specifically, the gains from Qwen2.5-VL-7B to the central part of the table are higer than those observed toward the end of it. Can the authors elaborate on this?\n\n**Q3:** Implementation details are completely omitted. Key information such as the **duration of the generated videos, the number of input frames provided to MLLMs for evaluation**, and whether frame sampling is needed (and if so, which one) is entirely missing.  These are not just details but important to understand the solidity of this work. \n\n**Q4:** The **data collection** is formulated as an optimization problem, yet the procedure seems mostly human-curated. It is not clear how the mapping T(.) is formulated. What does it mean that can be performed by either humans or LMMs? What is that exactly? Moreover, the actual hyperparameters of the constraints ($\\epsilon$, $\\delta$)are not stated, and it is not clear how they have been decided. Finally, what is the exact MLLM used in this step? I am not able to find this information, but I may be missing it. \n\n**Q5:** Why did the authors not send this paper to the **Datasets and Benchmarks** track? This paper explicitly proposes a benchmarking suite (VideoHallu) and evaluates state-of-the-art models on it. Given the existence of a dedicated track with clear guidelines, including requirements for code documentation, dataset formatting, and open-sourcing by the main deadline, this raises the question of whether this submission is best suited to the current track. At a minimum, the inclusion of code and at least a sample of the dataset in the supplementary materials would have been expected. It would be helpful to understand the authors' perspective on this decision.\n\n**Q6:** There are no information on **computing resources** used. Although in the checklist the answer to the question 8 is 'Yes', it seems that there is no information on the number and type of GPU needed for the evaluation and for the reinforcement fine-tuning. This input is needed.\n\nLIMITATIONS:\nThe authors include limitations in the conclusions, however, this is a 2-line comment that could be further extended at least in the Appendix.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 3, "DATE": "2025-06-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents VideoHallu which is a benchmark to evaluate the critical (physics-based) thinking abilities of the multi-modal large language models (MLLMs). By using 3000 synthetic videos paired with counterintuitive QA, it evaluates and mitigate hallucinations in multi-modal large language models (MLLMs) where the generations can show abnormal or abnormal or counterintuitive visual phenomena (such as an object exploding and reassembling back). The paper presents an exhaustive evaluation strategy into multiple categories such as spatial-temporal inconsistency, physics violations, and commonsense inconsistencies. Multiple state-of-the-art MLLMs are evaluated based on these metrics and the paper discusses several insights such as the MLLMs fail to answer counterintuitive questions, chain of thought also underperforms, and solely based on real-world data can create a bias of how the real-world works and limiting the understanding of counterintuitive physical phenomenon. \n\nTo address this, the paper proposes a curriculum learning-based finetuning strategy using general videos, real-world physics videos, and synthetic video QA data to improve physical understanding of the models. The evaluation shows modest gains in performance through this approach.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The idea of using counterintuitive examples (such as bullet being shot into a watermelon) is interesting.\n2. The paper is well-written and clearly explains how synthetic data can be helpful to provide counterintuitive examples and thus ensure physical plausibility.\n3. The paper presents an exhaustive set of categories and subcategories for evaluation of the physics on the basis of alignment, spatial-temporal consistency, common-sense reasoning, and physics.\n\nWeakness:\nWhile the idea of the paper of using counterintuitive questions/examples via synthetic data is sound, the results from the proposed contributions of curriculum training with general videos+real-world videos+synthetic videos and using GRPO in Table 3 doesn't seem to add much strength to the contributions and don't show much performance improvement on the proposed evaluation categories of alignment, spatial-temporal consistency, and physics. While the overall score is in favor of curriculum training using physics-real world and synthetic data, if we look at the results for each category in Table 3, for example in physics, for some metrics, training separately works better and for some metrics, curriculum training works better. The inconsistency raises some doubt as to whether curriculum learning is helpful or can training separately can be helpful in general. Similarly in alignment, the previous SoTA methods seem to show better performance majorly. Can the authors discuss why the performance is still on lower end when compared to previous SoTA methods?\n\nQUESTIONS:\nWill the VideoHallu dataset be publicly released with all associated QA pairs and metadata?\n\nLIMITATIONS:\nNot sure. The question 10: broader impact of the checklist has answer as [NA].", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- This work introduces VideoHallu, a video hallucination benchmark containing 3K QA pairs for synthetic videos created by modern video generation models. The goal is\n    \n- They define four evaluation categories, then employ human experts to create 141 adversarial prompts with which they generate 987 videos across seven video generation models.\n    \n- Question answer pairs are then created for each video by human experts and categorized into sub-categories\n    \n- They use LLM as a judge for evaluation, finding that frontier MLLMs struggle with counterintuitive questions and counterfactual entity changes\n    \n- Reasoning models do not fare better on these synthetic videos, having been trained only on real-world videos, so treating real-world rules as universal\n    \n- Propose to improve reasoning on synthetic videos via a three-stage curriculum which trains on two real-world datasets followed by the synthetic VideoHallu. They use GRPO with Answer-Equivalence BERT to assess accuracy and a BERTScore-like cosine similarity for the reward signal when training Qwen 2.5 VL\n    \n- The proposed curriculum training, especially the physics subset, improves performance on their benchmark\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n- Strong dataset contribution with human-crafted prompts and adversarial QA pairs organized into a taxonomy\n    \n- Important contribution in noting MLLMs’ deficiencies on synthetic videos, despite strong performance on real-world videos\n    \n- Evaluations show that the proposed training regime improves performance over the baseline model\n    \n\n# Weaknesses\n\n- The benefits of the proposed curriculum and use of reinforcement finetuning are unclear. Therefore, one is unsure whether the gains should be attributed to the training data mixture or the training strategy\n    \n    - Curriculum learning is compared to training on single subsets. However, a better comparison would be to train the base model on a mixture of the three datasets to evaluate the usefulness of curriculum learning in this setting\n        \n    - There appears to be no SFT baseline, so GRPO’s usefulness here is unclear.\n        \n- Without knowing whether the training strategy makes a significant difference over batched SFT, the takeaways of Table 3 and Figure 5 seem to be reduced to “training on synthetic video data improves performance on a synthetic video benchmark over the base models”\n\n- No evaluations on real-world video datasets to ensure regression does not occur after training on only synthetic video data\n\nQUESTIONS:\nPlease see the weaknesses. If these evaluation limitations are addressed I would be happy to raise my score\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}