pith. sign in

arxiv: 2606.27187 · v1 · pith:5XZMLETFnew · submitted 2026-06-25 · 💻 cs.CV · cs.CL

HarmVideoBench: Benchmarking Harmful Video Understanding in Large Multimodal Models

Jiajun Wu , Haoyu Kang , Yining Sun , Jiacheng Hou , Heng Zhang , Danyang Zhang , Zhenjun Zhao , Haochi Zhang
show 8 more authors
Leixin Sun Eric Hanchen Jiang Yushan Li Ruiyu Li Mengkai Huang Yan Gao Xu Zhang Guancheng Wan
This is my paper

Pith reviewed 2026-06-26 05:46 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords harmful video understandinglarge multimodal modelsbenchmarkcontent moderationhierarchical dimensionsvision-language modelsreasoning boundaries
0
0 comments X

The pith

HarmVideoBench introduces a multi-layered benchmark for harmful video understanding in large multimodal models, with BCR raising macro average accuracy from 61.7 to 84.4 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to move evaluation of harmful videos beyond binary classification by creating a benchmark that tests three hierarchical layers of understanding. This addresses the risk that models succeed through superficial shortcuts without grasping implicit or contextual harms, and without explaining their decisions. HarmVideoBench supplies 1,379 videos and 4,137 balanced multiple-choice questions across Observable Evidence, Clip-Internal Meaning, and Beyond-Clip Reasoning. The authors also introduce BCR, a method that predicts reasoning boundaries and retrieves extra context only when needed, which lifts performance on the new benchmark.

Core claim

HarmVideoBench is a diagnostic benchmark comprising 1,379 videos paired with 4,137 multiple-choice questions organized into three hierarchical dimensions—Observable Evidence, Clip-Internal Meaning, and Beyond-Clip Reasoning—to evaluate deep understanding of harmful content rather than surface cues. Existing benchmarks are limited to binary tasks without explanatory rationales. The BCR method predicts reasoning boundaries and dynamically retrieves context only when needed, raising the macro average from 61.7 percent to 84.4 percent across evaluations of 19 leading models.

What carries the argument

HarmVideoBench, the benchmark that structures evaluation around three hierarchical dimensions of harmful video understanding with explanatory rationales required at each level.

If this is right

  • Evaluation of multimodal models shifts from single binary labels to measurement across observable, internal, and extended reasoning layers.
  • Models are now required to supply explanatory rationales, reducing success via black-box shortcuts.
  • BCR enables performance gains on base models by limiting context retrieval to cases where reasoning boundaries indicate it is necessary.
  • The benchmark supplies a standardized way to compare 19 leading models on multidimensional harmful-video capabilities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the dimensions prove robust, they could be reused to test understanding depth in non-harm video domains such as instructional or news content.
  • BCR's boundary prediction step might reduce compute costs when applied to other multimodal tasks that sometimes need extra context.
  • Widespread adoption could push moderation systems toward handling implicit harms that current binary detectors miss.

Load-bearing premise

The 1,379 videos and 4,137 questions are balanced and curated such that success on the three dimensions measures genuine deep understanding rather than superficial cues or annotation artifacts.

What would settle it

A model that scores high on HarmVideoBench after training only on the question text without access to the videos, or that fails on new videos containing similar harms but different surface features, would show the benchmark does not test deep understanding.

read the original abstract

Large vision-language models (LVLMs) have recently shown immense potential in automated content moderation, sparking growing interest in developing harmful-video benchmarks. However, we identify two primary limitations in existing works: 1) The multi-layered characteristics of harmful videos are overlooked. Existing benchmarks predominantly formulate evaluation as a binary classification task, failing to capture implicit or deep contextual harms. 2) Explanatory rationales are completely absent. Current frameworks measure exclusively whether a model flags a video correctly rather than explaining why, turning evaluation into a black box where models can succeed through superficial shortcuts. To address these problems, we present HarmVideoBench, a multi-layered diagnostic benchmark comprising 1,379 videos paired with 4,137 multiple-choice questions. HarmVideoBench benchmarks three hierarchical dimensions: Observable Evidence, Clip-Internal Meaning, and Beyond-Clip Reasoning, aiming to evaluate models' deep understanding beyond surface cues with carefully balanced and curated samples. We evaluate 19 leading models on HarmVideoBench to assess their multidimensional understanding of harmful videos. Moreover, we introduce BCR, a benchmark-aligned method that predicts reasoning boundaries and dynamically retrieves context only when needed. Experimental results show that BCR substantially improves the base model's performance in harmful video understanding, raising the macro average from 61.7 percent to a state-of-the-art 84.4 percent.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The manuscript introduces HarmVideoBench, a multi-layered diagnostic benchmark consisting of 1,379 videos and 4,137 multiple-choice questions designed to evaluate large vision-language models on harmful video understanding across three hierarchical dimensions: Observable Evidence, Clip-Internal Meaning, and Beyond-Clip Reasoning. It evaluates 19 leading models and proposes BCR, a benchmark-aligned method that predicts reasoning boundaries and dynamically retrieves context, reporting an improvement in macro-average performance from 61.7% to 84.4%.

Significance. If the benchmark curation is shown to be free of superficial cues and annotation artifacts, HarmVideoBench would provide a valuable advance over existing binary-classification harmful-video benchmarks by enabling diagnostic evaluation of nuanced, multi-layered understanding. BCR's reported gains would then indicate a promising direction for efficient, selective context use in multimodal reasoning. The multi-model evaluation offers a useful baseline for the field.

major comments (3)
  1. [Abstract and HarmVideoBench construction] Abstract and HarmVideoBench construction section: the claim that the 1,379 videos and 4,137 questions are 'carefully balanced and curated' to avoid surface cues and measure deep understanding is load-bearing for all performance claims, yet the manuscript supplies no quantitative evidence such as inter-annotator agreement, adversarial filtering results, human baselines per dimension, or correlation analyses between accuracy and superficial features (video length, keyword presence).
  2. [BCR method and experimental results] BCR method and experimental results section: BCR is described as 'benchmark-aligned,' which creates a circularity risk for the reported 61.7% to 84.4% macro-average lift; the paper must demonstrate via ablations or controls that gains arise from reasoning-boundary prediction rather than exploitation of benchmark-specific patterns.
  3. [Evaluation protocol] Evaluation protocol section: without reported statistical controls, variance estimates, or per-dimension human performance, it is impossible to determine whether the 84.4% figure reflects genuine hierarchical reasoning or benchmark artifacts, directly affecting the central claim that BCR achieves state-of-the-art harmful-video understanding.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments, which highlight important areas for strengthening the empirical support of our claims. We address each major comment below and commit to revisions that provide the requested quantitative evidence and controls.

read point-by-point responses

  1. Referee: [Abstract and HarmVideoBench construction] Abstract and HarmVideoBench construction section: the claim that the 1,379 videos and 4,137 questions are 'carefully balanced and curated' to avoid surface cues and measure deep understanding is load-bearing for all performance claims, yet the manuscript supplies no quantitative evidence such as inter-annotator agreement, adversarial filtering results, human baselines per dimension, or correlation analyses between accuracy and superficial features (video length, keyword presence).

    Authors: We agree that the absence of quantitative validation metrics for the curation process weakens the load-bearing claim. In the revised manuscript we will add: (1) inter-annotator agreement scores computed across the annotation pipeline, (2) results from adversarial filtering experiments that removed superficial cues, (3) per-dimension human performance baselines, and (4) correlation analyses between model accuracy and superficial features such as video length and keyword presence. These additions will supply the requested empirical support. revision: yes

  2. Referee: [BCR method and experimental results] BCR method and experimental results section: BCR is described as 'benchmark-aligned,' which creates a circularity risk for the reported 61.7% to 84.4% macro-average lift; the paper must demonstrate via ablations or controls that gains arise from reasoning-boundary prediction rather than exploitation of benchmark-specific patterns.

    Authors: We acknowledge the circularity concern. The revised manuscript will include new ablation studies that isolate the contribution of reasoning-boundary prediction: comparisons against random-context and fixed-context retrieval baselines, as well as controls that test performance on held-out or synthetically altered benchmark subsets designed to detect pattern exploitation. These experiments will demonstrate that the reported gains derive from adaptive context use rather than benchmark-specific overfitting. revision: yes

  3. Referee: [Evaluation protocol] Evaluation protocol section: without reported statistical controls, variance estimates, or per-dimension human performance, it is impossible to determine whether the 84.4% figure reflects genuine hierarchical reasoning or benchmark artifacts, directly affecting the central claim that BCR achieves state-of-the-art harmful-video understanding.

    Authors: We agree that the current evaluation protocol lacks sufficient statistical rigor. The revision will report variance estimates across multiple evaluation runs, include statistical significance tests (e.g., paired t-tests) for all performance deltas, and add per-dimension human performance baselines. These additions will allow readers to assess whether the 84.4% result reflects genuine hierarchical reasoning improvements. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on empirical evaluation of new benchmark

full rationale

The paper introduces HarmVideoBench (1,379 videos, 4,137 questions across three hierarchical dimensions) and BCR as a benchmark-aligned method, then reports macro-average gains from 61.7% to 84.4% via evaluation of 19 models. No equations, fitted parameters renamed as predictions, self-citations, or uniqueness theorems are present in the provided text that would reduce the performance claim to a definitional tautology or construction from the benchmark inputs themselves. The derivation chain is self-contained as an empirical reporting step on a newly curated dataset.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claims rest on the assumption that the curated videos and questions form a valid diagnostic without introducing new biases; no free parameters or invented physical entities are described in the abstract.

invented entities (1)
  • BCR no independent evidence
    purpose: Predicts reasoning boundaries and dynamically retrieves context to improve harmful video understanding
    Introduced in the abstract as the method that achieves the reported performance lift

pith-pipeline@v0.9.1-grok · 5822 in / 1181 out tokens · 27873 ms · 2026-06-26T05:46:18.614756+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

23 extracted references · 14 canonical work pages · 8 internal anchors

  1. [1]

    Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J

    Marah I Abdin, Jyoti Aneja, Harkirat S. Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J. Hewett, Mojan Javaheripi, Piero Kauffmann, James R. Lee, Yin Tat Lee, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingl...

    Pith/arXiv arXiv

  2. [2]

    Phi-4 Technical Report

    doi: 10.48550/ARXIV.2412.08905. URLhttps://doi.org/10. 48550/arXiv.2412.08905. Anthropic. Introducing claude opus 4.7.https://www.anthropic.com/news/claude-opus-4-7, April 2026a. Anthropic. Introducing claude sonnet 4.6. https://www.anthropic.com/news/ claude-sonnet-4-6, February 2026b. Bilibili Inc. 2024 annual report.https://www.hkexnews.hk/listedco/lis...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2412.08905 2024

  3. [3]

    Lance: Unified multimodal modeling by multi-task synergy.CoRR, abs/2605.18678,

    ByteDance Intelligent Creation Team. Lance: Unified multimodal modeling by multi-task synergy.CoRR, abs/2605.18678,

    Pith/arXiv arXiv

  4. [4]

    Lance: Unified Multimodal Modeling by Multi-Task Synergy

    doi: 10.48550/arXiv.2605.18678. URL https://arxiv.org/abs/2605. 18678. Mithun Das, Rohit Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, and Animesh Mukherjee. Hatemm: A multi-modal dataset for hate video classification. In Yu-Ru Lin, Meeyoung Cha, and Daniele Quercia, editors,Proceedings of the Seventeenth International AAAI Conference on Web and Social ...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605.18678 2023

  5. [5]

    Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

    doi: 10.1609/ICWSM.V17I1.22209. URLhttps://doi.org/10.1609/icwsm.v17i1.22209. Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rongrong Ji, and Xing Sun. Video-mme: The first-ever comprehen...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1609/icwsm.v17i1.22209

  6. [6]

    Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

    doi: 10.48550/ARXIV. 2405.21075. URLhttps://doi.org/10.48550/arXiv.2405.21075. Google. Gemini 3 flash: Frontier intelligence built for speed. https://blog.google/products/ gemini/gemini-3-flash, December

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv

  7. [7]

    SURE: Safety understanding and reasoning enhancement for multimodal large language models

    Yuxin Gou, Xiaoning Dong, Qin Li, Shishen Gu, Richang Hong, and Wenbo Hu. SURE: Safety understanding and reasoning enhancement for multimodal large language models. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 7552–7593, Suzhou, China, November

    2025

  8. [8]

    doi: 10.18653/v1/2025.emnlp-main.384

    Association for Computational Linguistics. doi: 10.18653/v1/2025.emnlp-main.384. URLhttps: //aclanthology.org/2025.emnlp-main.384/. Wenbo Hu, Shishen Gu, Youze Wang, and Richang Hong. Videojail: Exploiting video-modality vulnerabilities for jailbreak attacks on multimodal large language models. InICLR 2025 Workshop on Building Trust in Language Models and...

    work page doi:10.18653/v1/2025.emnlp-main.384 2025

  9. [9]

    15 HarmVideoBench Kimi Team

    URLhttps://openreview.net/forum?id=fSAIDcPduZ. 15 HarmVideoBench Kimi Team. Kimi k2.5: Visual agentic intelligence.CoRR, abs/2602.02276,

    Pith/arXiv arXiv

  10. [10]

    doi: 10.48550/arXiv.2602. 02276. URLhttps://arxiv.org/abs/2602.02276. Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Lou, Limin Wang, and Yu Qiao. Mvbench: A comprehensive multi-modal video understanding benchmark. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, US...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2602 2024

  11. [11]

    V*: Guided visual search as a core mechanism in multimodal llms

    doi: 10.1109/CVPR52733.2024.02095. URLhttps: //doi.org/10.1109/CVPR52733.2024.02095. Hongzhan Lin, Ziyang Luo, Jing Ma, and Long Chen. Beneath the surface: Unveiling harmful memes with multimodal reasoning distilled from large language models. In Houda Bouamor, Juan Pino, and Kalika Bali, editors,Findings of the Association for Computational Linguistics: ...

    work page doi:10.1109/cvpr52733.2024.02095 2024

  12. [12]

    LLM-blender: Ensembling large language models with pairwise ranking and generative fusion

    doi: 10.18653/V1/2023. FINDINGS-EMNLP.611. URLhttps://doi.org/10.18653/v1/2023.findings-emnlp.611. Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner. Reasoning over paragraph effects in situations. arXiv preprint arXiv:1908.05852,

    work page doi:10.18653/v1/2023 2023

  13. [13]

    Mm-safetybench: A benchmark for safety evaluation of multimodal large language models

    Xin Liu, Yichen Zhu, Jindong Gu, Yunshi Lan, Chao Yang, and Yu Qiao. Mm-safetybench: A benchmark for safety evaluation of multimodal large language models. In Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, and Gül Varol, editors,Computer Vision – ECCV 2024, pages 386–403, Cham, 2025a. Springer Nature Switzerland. ISBN 978-3-0...

    arXiv 2024

  14. [14]

    Qwen3-vl technical report.CoRR, abs/2511.21631,

    Qwen Team. Qwen3-vl technical report.CoRR, abs/2511.21631,

    Pith/arXiv arXiv

  15. [15]

    Qwen3-VL Technical Report

    doi: 10.48550/arXiv.2511.21631. URLhttps://arxiv.org/abs/2511.21631. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. Socialiqa: Commonsense reasoning about social interactions.arXiv preprint arXiv:1904.09728,

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2511.21631 1904

  16. [16]

    what if

    Niket Tandon, Bhavana Dalvi Mishra, Keisuke Sakaguchi, Antoine Bosselut, and Peter Clark. Wiqa: A dataset for" what if..." reasoning over procedural text.arXiv preprint arXiv:1909.04739,

    arXiv 1909

  17. [17]

    URL https://www.nature.com/articles/ s41598-022-11488-y

    doi: 10.1038/s41598-022-11488-y. URL https://www.nature.com/articles/ s41598-022-11488-y. Han Wang, Tan Rui Yang, Usman Naseem, and Roy Ka-Wei Lee. Multihateclip: A multilingual benchmark dataset for hateful video detection on youtube and bilibili. In Jianfei Cai, Mohan S. Kankanhalli, Balakrish- nan Prabhakaran, Susanne Boll, Ramanathan Subramanian, Lian...

    work page doi:10.1038/s41598-022-11488-y 2024

  18. [18]

    URLhttps://doi.org/10.1145/3664647.3681521

    doi: 10.1145/3664647.3681521. URLhttps://doi.org/10.1145/3664647.3681521. Wenxuan Wang, Xiaoyuan Liu, Kuiyi Gao, Jen-tse Huang, Youliang Yuan, Pinjia He, Shuai Wang, and Zhaopeng Tu. Can’t see the forest for the trees: Benchmarking multimodal safety awareness for multimodal LLMs. InProceedings of the 63rd Annual Meeting of the Association for Computationa...

    work page doi:10.1145/3664647.3681521 2025

  19. [19]

    Jun Xu, Tao Mei, Ting Yao, and Yong Rui

    URL http://papers.nips.cc/paper_ files/paper/2024/hash/329ad516cf7a6ac306f29882e9c77558-Abstract-Datasets_ and_Benchmarks_Track.html. Jun Xu, Tao Mei, Ting Yao, and Yong Rui. MSR-VTT: A large video description dataset for bridging video and language. In2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-...

    2024

  20. [20]

    ISBN 978-1-4673-8851-1

    doi: 10.1109/CVPR.2016.571. URLhttps://doi.org/10.1109/CVPR.2016.571. ZhouYu, DejingXu, JunYu, TingYu, ZhouZhao, YuetingZhuang, andDachengTao. Activitynet-qa: Adataset for understanding complex web videos via question answering. InThe Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial...

    work page doi:10.1109/cvpr.2016.571 2016

  21. [21]

    GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

    doi: 10.1609/AAAI. V33I01.33019127. URLhttps://doi.org/10.1609/aaai.v33i01.33019127. Z.AI Team. Glm-4.1v-thinking and glm-4.5v: Towards versatile multimodal reasoning with scalable reinforcement learning.CoRR, abs/2507.01006,

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1609/aaai

  22. [22]

    GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

    doi: 10.48550/arXiv.2507.01006. URL https://arxiv.org/abs/2507.01006. 17 HarmVideoBench Yongting Zhang, Lu Chen, Guodong Zheng, Yifeng Gao, Rui Zheng, Jinlan Fu, Zhenfei Yin, Senjie Jin, Yu Qiao, Xuanjing Huang, Feng Zhao, Tao Gui, and Jing Shao. Spa-vl: A comprehensive safety preference alignment dataset for vision language models. InProceedings of the I...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2507.01006

  23. [23]

    question_id

    Each sampled item is independently reviewed under the same three-category taxonomy used during benchmark construction, and disagreements are resolved by the senior adjudicator. We report agreement by reasoning category rather than only as a single aggregate number, because the main source of ambiguity is not low-level perception but the boundary between c...

    2026