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arxiv: 2606.31187 · v1 · pith:OUNPEGMOnew · submitted 2026-06-30 · 💻 cs.CV

Learning to Deny: Action Denial in Multimodal Large Language Models

Raiyaan Abdullah , Shehreen Azad , Yogesh Singh Rawat This is my paper

Pith reviewed 2026-07-01 06:19 UTC · model grok-4.3

classification 💻 cs.CV
keywords action denialmultimodal large language modelsvideo understandingcausal reasoningbenchmarkUCF101false positive reduction
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The pith

Multimodal models that recognize actions above 85 percent accuracy drop below 50 percent when asked to deny those same actions in matched videos.

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

The paper creates UCF101-AD, a benchmark of video pairs that share persons, objects, and locations but differ only in whether a target action occurs or is absent. Testing twenty current multimodal large language models shows strong performance on confirming actions but consistent failure on denying them, pointing to reliance on contextual plausibility rather than verification of actual motion. The authors formulate scene structure as a causal graph expressed in natural language prompts and show that this reduces incorrect affirmations. The result frames denial as a distinct reasoning skill that current models lack. This benchmark isolates the gap between presence detection and absence confirmation in video understanding.

Core claim

Evaluating twenty state-of-the-art MLLMs on UCF101-AD reveals that models exceeding 85 percent accuracy on positive action classes collapse below 50 percent on their action-denial counterparts; expressing scene structure through natural-language prompts that link context, interaction, and motion via the CausalAct formulation substantially reduces false positives, showing that denial is a learnable reasoning skill.

What carries the argument

UCF101-AD benchmark of paired positive and negative video clips that preserve identical contextual and motion cues while making the target action explicitly absent, used to isolate denial performance from visual differences.

If this is right

  • Standard action-recognition benchmarks overestimate model capability because they do not test denial of plausible but absent actions.
  • Denial accuracy improves when models receive explicit natural-language prompts that connect context to interaction to motion.
  • Video understanding systems require separate verification of motion occurrence rather than inference from surrounding cues alone.
  • The gap between presence and denial performance is consistent across current MLLM architectures.

Where Pith is reading between the lines

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

  • Applications such as automated surveillance or video search could produce systematic false alarms when models cannot reliably report that an expected action did not occur.
  • Training regimes that only reward correct affirmation of actions may reinforce the observed bias against denial.
  • The same paired-context design could be applied to other modalities or tasks to test whether causal verification failures are widespread.

Load-bearing premise

The negative videos keep the same persons, objects, and locations as the positive ones, differing only by the explicit absence of the target action.

What would settle it

Finding that negative videos contain systematic differences in low-level motion statistics or object trajectories that models could use to separate them from positive clips without causal reasoning.

Figures

Figures reproduced from arXiv: 2606.31187 by Raiyaan Abdullah, Shehreen Azad, Yogesh Singh Rawat.

Figure 1
Figure 1. Figure 1: Model failures in denying an action: (left) Models are capable of rec￾ognizing when the action occurs, but seeing a person holding a marker in front of a board but not writing, they still predict “Writing On Board”. (right) Radar plot of accuracy on the original UCF101 test set (green) vs. the UCF101-AD Action-Denial videos (red). Most models exceed 85% on UCF101 but fall below 50% on videos where the acti… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of UCF101-AD. The dataset contains Action-Presence clips and hard Action-Denial negatives. Negatives come in two types: Type 1 - Context only, where the usual scene and objects are present but the defining motion is absent, and Type 2 - Same context, different motion, where a different action occurs in the same setting. For the task where the model must select the correct caption for the video, we… view at source ↗
Figure 3
Figure 3. Figure 3: Effect of progressive hinting on action denial capability. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of dual￾failure correlations. Pearson’s r coefficient correlates each model’s shortcut learning error with its sycophancy error showing a positive correlation (r > 0). General vs. Thinking models. Our analysis of the negative videos in the UCF101-ActionDenial dataset reveals a counterintuitive pattern: reasoning mod￾els generally perform worse than their standard counterparts (except Ovis2.5).… view at source ↗
Figure 5
Figure 5. Figure 5: CausalAct showing the compo￾nents of an action scene: Persons (P), Objects (O), and Location (L) are contextual nodes; Spatial Relation (S) and Interaction (I) are relational nodes; Motion (M) is the dy￾namic node; and Action (A) is the final activity label. Intuitively, contextual elements (persons, objects, and environment) deter￾mine the spatial configuration of the scene, which enables potential intera… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of baseline, CausalAct-0, and CausalAct. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison of the baseline vs. CausalAct. The base Qwen2.5-VL-3B-Instruct takes shortcuts based on contextual cues in the scene, whereas CausalAct correctly leverages the graph structure to verify the defining motion, en￾abling it to deny the non-existent action for both Not Playing Daf and Not Floor Gymnastics [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation study on graph structure: We compare CausalAct to a Pruned Graph (P-O-I-A only) and a Random Graph (shuffled edges). Numeric annotations quantify decline (-) or improvement (+) relative to CausalAct. Is the object node necessary for person-only actions? To test this, we evaluate a reduced graph that omits object and interaction nodes (mapping only Person, Location, Spatial Relation, Motion, and Ac… view at source ↗
Figure 9
Figure 9. Figure 9: Effect of removing object nodes for person-only actions. Base￾line and CausalAct-0 are evaluated on all Action-Denial videos, while the Reduced Graph is evaluated on body and location￾based actions. Comparable performance shows the causal structure remains ef￾fective without explicit object-interaction reasoning. 5 Conclusion We introduce UCF101-AD, a benchmark targeting a critical failure of modern MLLMs:… view at source ↗
Figure 10
Figure 10. Figure 10: Overview of the video curation pipeline for UCF101-AD Action-Denial videos. For Action-Denial clips, we began by defining negative classes correspond￾ing to the UCF101 actions and querying YouTube with keywords designed to retrieve videos that preserve similar contextual cues, including people, objects, and locations, while excluding the defining motion of the target action. The re￾trieved videos were the… view at source ↗
Figure 11
Figure 11. Figure 11: Illustration of the evaluation and analytical variants. Standard [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Overview of CausalAct. Top: The CausalAct representation organizes video understanding components as nodes in a directed acyclic graph (DAG), which is then used to construct the detailed prompt provided to the MLLM. Bottom: Dur￾ing graph-based question-answer finetuning, Action-Denial training video information is converted into templated questions covering graph topology, node relations, causal paths, an… view at source ↗
read the original abstract

Multimodal large language models (MLLMs) have rapidly advanced video understanding, achieving strong zero-shot and few-shot recognition across standard benchmarks. Yet their ability to deny an action by recognizing when an activity is not happening despite strong contextual cues remains largely unexplored. We introduce UCF101-AD, a large-scale benchmark consisting of paired Action-Presence and Action-Denial clips, designed to evaluate this capacity for denial. Each negative video in UCF101-AD preserves the same contextual and motion cues, including persons, objects, and locations, as its positive counterpart, but the defining action itself is explicitly absent. Evaluating 20 state-of-the-art MLLMs reveals a consistent failure: models that exceed 85% accuracy on the positive action classes collapse below 50% on their action-denial counterparts, indicating a strong inclination to affirm plausible actions rather than verify that they truly occur. This exposes a critical blind spot in modern video understanding: the inability to reason causally about whether a motion actually happens. To probe this issue, we explore a causal graph formulation, CausalAct, which expresses scene structure through natural-language prompts linking context, interaction, and motion. Incorporating such causal cues substantially reduces false positives, demonstrating that denial is a learnable reasoning skill. UCF101-AD provides a new lens for diagnosing and improving causal reasoning in multimodal models. Dataset and relevant code: https://github.com/raiyaan-abdullah/Learn-to-Deny.

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

1 major / 1 minor

Summary. The paper introduces UCF101-AD, a benchmark of paired positive and negative action videos from UCF101 where each negative clip is claimed to preserve identical persons, objects, locations, and non-defining motion cues while making the target action explicitly absent. It reports that 20 state-of-the-art MLLMs achieve >85% accuracy on positive classes but collapse below 50% on the corresponding denial tasks, and proposes CausalAct, a natural-language causal-graph prompting method that reduces false positives by linking context, interaction, and motion.

Significance. If the negative-video construction truly isolates action absence, the work identifies a reproducible and previously unquantified limitation in current MLLMs' causal verification of video actions and shows that the limitation is at least partially addressable via explicit causal prompting. The new benchmark and the CausalAct formulation constitute a concrete, falsifiable contribution to the evaluation of multimodal reasoning.

major comments (1)
  1. [Abstract / UCF101-AD description] Abstract and UCF101-AD construction section: the central interpretive claim—that the observed 85%→<50% accuracy collapse demonstrates a failure of causal verification rather than ordinary distribution shift—rests on the assertion that negatives preserve identical contextual and motion cues while differing only in the explicit absence of the target action. No construction protocol, quantitative similarity metric (e.g., optical-flow or feature-space distance), or human validation study is supplied, so the performance gap cannot yet be attributed specifically to missing causal reasoning.
minor comments (1)
  1. The GitHub link for dataset and code is a positive reproducibility feature and should be retained.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading and for highlighting the need to strengthen the evidentiary basis for UCF101-AD. We address the single major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract / UCF101-AD description] Abstract and UCF101-AD construction section: the central interpretive claim—that the observed 85%→<50% accuracy collapse demonstrates a failure of causal verification rather than ordinary distribution shift—rests on the assertion that negatives preserve identical contextual and motion cues while differing only in the explicit absence of the target action. No construction protocol, quantitative similarity metric (e.g., optical-flow or feature-space distance), or human validation study is supplied, so the performance gap cannot yet be attributed specifically to missing causal reasoning.

    Authors: We agree that the current manuscript does not supply sufficient detail on the negative-video construction process. In the revised version we will add an expanded Section 3 that (i) describes the exact protocol used to select and edit UCF101 clips so that persons, objects, locations and non-defining motion are matched while the target action is removed, (ii) reports quantitative similarity metrics (optical-flow L2 distance, CLIP feature cosine similarity, and scene-graph overlap) between each positive–negative pair, and (iii) presents the results of a human validation study (N=200 raters) confirming that contextual cues are preserved while the defining action is judged absent. These additions will allow readers to evaluate whether the observed accuracy drop is attributable to causal-verification failure rather than distribution shift. revision: yes

Circularity Check

0 steps flagged

No circularity: results derive from independent benchmark evaluation

full rationale

The paper introduces the UCF101-AD benchmark and reports empirical accuracies of 20 MLLMs on its positive and negative classes. No equations, parameter fits, predictions, or self-citations are invoked that would reduce the reported performance gap to the input data by construction. The central interpretation (affirmation bias vs. causal verification failure) rests on the stated properties of the new dataset rather than any self-definitional or fitted-input mechanism. This is a standard empirical study whose claims are falsifiable against the released dataset and do not collapse into their own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Only the abstract is available, so the full set of modeling choices cannot be audited. The main introduced element is the CausalAct prompting approach.

invented entities (1)
  • CausalAct no independent evidence
    purpose: Expresses scene structure through natural-language prompts linking context, interaction, and motion to reduce false positives on denial
    Described as a causal graph formulation incorporated into prompts

pith-pipeline@v0.9.1-grok · 5799 in / 1079 out tokens · 33571 ms · 2026-07-01T06:19:02.494245+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

131 extracted references · 30 canonical work pages

  1. [1]

    punching person: Motion transferability in videos

    Abdullah, R., Claypoole, J., Cogswell, M., Divakaran, A., Rawat, Y.: Punching bag vs. punching person: Motion transferability in videos. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 11348– 11358 (October 2025)

    2025

  2. [2]

    In: Proceedings of the IEEE/CVF International ConferenceonComputerVision(ICCV)Workshops.pp.1433–1442(October2025)

    Abdullah, R., Rawat, Y.S., Vyas, S.: isafetybench: A video-language benchmark for safety in industrial environment. In: Proceedings of the IEEE/CVF International ConferenceonComputerVision(ICCV)Workshops.pp.1433–1442(October2025)

  3. [3]

    Transactions on Machine Learning Research (2025), https://openreview.net/forum?id=WvgoxpGpuU 16 R

    Ahmad, S., Chanda, S., Rawat, Y.S.: T2l: Efficient zero-shot action recognition with temporal token learning. Transactions on Machine Learning Research (2025), https://openreview.net/forum?id=WvgoxpGpuU 16 R. Abdullah et al

    2025

  4. [4]

    Bagheri, A., Alinejad, M., Bello, K., Akhondi-Asl, A.: C2P: Featuring large lan- guage models with causal reasoning (2024),https://arxiv.org/abs/2407.18069

    arXiv 2024

  5. [5]

    Bai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., Tang, J., Zhong, H., Zhu, Y., Yang, M., Li, Z., Wan, J., Wang, P., Ding, W., Fu, Z., Xu, Y., Ye, J., Zhang, X., Xie, T., Cheng, Z., Zhang, H., Yang, Z., Xu, H., Lin, J.: Qwen2.5-vl technical report (2025),https://arxiv.org/abs/2502.13923

    Pith/arXiv arXiv 2025

  6. [6]

    4724–4733 (2017),https://api.semanticscholar.org/CorpusID: 206596127

    Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the ki- neticsdataset.2017IEEEConferenceonComputerVisionandPatternRecognition (CVPR) pp. 4724–4733 (2017),https://api.semanticscholar.org/CorpusID: 206596127

    2017

  7. [7]

    (eds.) Findings of the Association for Computational Linguis- tics: EMNLP 2024

    Chen, S., Xu, M., Wang, K., Zeng, X., Zhao, R., Zhao, S., Lu, C.: CLEAR: Can language models really understand causal graphs? In: Al-Onaizan, Y., Bansal, M., Chen, Y.N. (eds.) Findings of the Association for Computational Linguis- tics: EMNLP 2024. pp. 6247–6265. Association for Computational Linguistics, Mi- ami, Florida, USA (Nov 2024).https://doi.org/1...

    work page doi:10.18653/v1/2024.findings- 2024

  8. [8]

    Advances in Neural Information Processing Systems37, 92554–92580 (2024)

    Chen, T., Liu, H., He, T., Chen, Y., Ma, X., Zhong, C., Zhang, Y., Wang, Y., Lin, H., Lin, W., et al.: Mecd: Unlocking multi-event causal discovery in video reasoning. Advances in Neural Information Processing Systems37, 92554–92580 (2024)

    2024

  9. [9]

    arXiv preprint arXiv:2412.05271 (2024)

    Chen, Z., Wang, W., Cao, Y., Liu, Y., Gao, Z., Cui, E., Zhu, J., Ye, S., Tian, H., Liu, Z., et al.: Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271 (2024)

    Pith/arXiv arXiv 2024

  10. [10]

    In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R

    Choi, J., Gao, C., Messou, J.C.E., Huang, J.B.: Why can't i dance in the mall? learning to mitigate scene bias in action recognition. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Ad- vances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019),https://proceedings.neurips.cc/pape...

    2019

  11. [11]

    Lawrence Erl- baum Associates, Hillsdale, NJ, 2nd edn

    Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Lawrence Erl- baum Associates, Hillsdale, NJ, 2nd edn. (1988)

    1988

  12. [12]

    In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)

    Damen, D., Doughty, H., Farinella, G.M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., Wray, M.: Scaling egocentric vision: The epic-kitchens dataset. In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)

    2018

  13. [13]

    In: Karlinsky, L., Michaeli, T., Nishino, K

    Duan, H., Zhao, Y., Chen, K., Xiong, Y., Lin, D.: Mitigating representation bias in action recognition: Algorithms and benchmarks. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision – ECCV 2022 Workshops. pp. 557–575. Springer Nature Switzerland, Cham (2023)

    2022

  14. [14]

    In: 2020 IEEE/CVF Confer- enceonComputerVisionandPatternRecognition,CVPR2020,Seattle,WA,USA, June 13-19, 2020

    Epstein, D., Chen, B., Vondrick, C.: Oops! predicting unintentional action in video. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 916–926 (2020).https://doi.org/10.1109/CVPR42600.2020.00100

    work page doi:10.1109/cvpr42600.2020.00100 2020

  15. [15]

    In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)

    Fang, J., Yan, D., Qiao, J., Xue, J., Wang, H., Li, S.: Dada-2000: Can driving accident be predicted by driver attentionƒanalyzed by a benchmark. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). p. 4303–4309. IEEE Press (2019).https://doi.org/10.1109/ITSC.2019.8917218,https://doi.org/10. 1109/ITSC.2019.8917218

    work page doi:10.1109/itsc.2019.8917218 2000

  16. [16]

    CoRRabs/2502.08177(Febru- Learning to Deny: Action Denial in Multimodal Large Language Models 17 ary 2025),http://dblp.uni-trier.de/db/journals/corr/corr2502.html#abs- 2502-08177

    Fanous, A., Goldberg, J., Agarwal, A.A., Lin, J., Zhou, A., Daneshjou, R., Koyejo, S.: Syceval: Evaluating llm sycophancy. CoRRabs/2502.08177(Febru- Learning to Deny: Action Denial in Multimodal Large Language Models 17 ary 2025),http://dblp.uni-trier.de/db/journals/corr/corr2502.html#abs- 2502-08177

    arXiv 2025

  17. [17]

    arXiv preprint arXiv:2503.21776 (2025)

    Feng, K., Gong, K., Li, B., Guo, Z., Wang, Y., Peng, T., Wang, B., Yue, X.: Video-r1: Reinforcing video reasoning in mllms. arXiv preprint arXiv:2503.21776 (2025)

    Pith/arXiv arXiv 2025

  18. [18]

    In: The Thirteenth International Conference on Learning Representations (2025)

    Fioresi, J., Dave, I.R., Shah, M.: Albar: Adversarial learning approach to miti- gate biases in action recognition. In: The Thirteenth International Conference on Learning Representations (2025)

    2025

  19. [19]

    (eds.) Mul- tiMedia Modeling

    Fukuzawa, T., Hara, K., Kataoka, H., Tamaki, T.: Can masking background and object reduce static bias for zero-shot action recognition? In: Ide, I., Kompatsiaris, I., Xu, C., Yanai, K., Chu, W.T., Nitta, N., Riegler, M., Yamasaki, T. (eds.) Mul- tiMedia Modeling. pp. 366–379. Springer Nature Singapore, Singapore (2025)

    2025

  20. [20]

    arXiv preprint arXiv:2507.20939 (2025)

    Ge, Y., Ge, Y., Li, C., Wang, T., Pu, J., Li, Y., Qiu, L., Ma, J., Duan, L., Zuo, X., et al.: Arc-hunyuan-video-7b: Structured video comprehension of real-world shorts. arXiv preprint arXiv:2507.20939 (2025)

    arXiv 2025

  21. [21]

    In: 2018 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition Workshops (CVPRW)

    Giancola, S., Amine, M., Dghaily, T., Ghanem, B.: SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos . In: 2018 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition Workshops (CVPRW). pp. 1792–179210. IEEE Computer Society, Los Alamitos, CA, USA (Jun 2018).https://doi.org/ 10.1109/CVPRW.2018.00223,https://doi.ieeecomputersociet...

    work page doi:10.1109/cvprw.2018.00223 2018

  22. [22]

    Gorti, S.K., Vouitsis, N., Ma, J., Golestan, K., Volkovs, M., Garg, A., Yu, G.: X- pool:Cross-modallanguage-videoattentionfortext-videoretrieval.In:Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

    2022

  23. [23]

    Something Something

    Goyal, R., Kahou, S.E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., Hoppe, F., Thurau, C., Bax, I., Memisevic, R.: The “Something Something” Video Database for Learning and Evaluating Visual Common Sense . In: 2017 IEEE International Conference on Computer Vision (ICCV). pp. 5843–5851. ...

    work page doi:10.1109/iccv.2017.622 2017

  24. [24]

    In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Ham- burger, J., Jiang, H., Liu, M., Liu, X., Martin, M., Nagarajan, T., Radosavovic, I., Ramakrishnan, S.K., Ryan, F., Sharma, J., Wray, M., Xu, M., Xu, E.Z., Zhao, C., Bansal, S., Batra, D., Cartillier, V., Crane, S., Do, T., Doulaty, M., Era- palli, A., Feichtenhofer, C., Frago...

    work page doi:10.1109/cvpr52688.2022.01842 2022

  25. [25]

    In: 2018 IEEE/CVF 18 R

    Gu, C., Sun, C., Ross, D.A., Vondrick, C., Pantofaru, C., Li, Y., Vijayanarasimhan, S., Toderici, G., Ricco, S., Sukthankar, R., Schmid, C., Malik, J.: Ava: A video dataset of spatio-temporally localized atomic visual actions. In: 2018 IEEE/CVF 18 R. Abdullah et al. Conference on Computer Vision and Pattern Recognition. pp. 6047–6056 (2018). https://doi.o...

    work page doi:10.1109/cvpr.2018.00633 2018

  26. [26]

    DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

    Guo, D., Yang, D., Zhang, H., Song, J., Wang, P., Zhu, Q., Xu, R., Zhang, R., Ma, S., Bi, X., Zhang, X., Yu, X., Wu, Y., Wu, Z.F., Gou, Z., Shao, Z., Li, Z., Gao, Z., Liu, A., Xue, B., Wang, B., Wu, B., Feng, B., Lu, C., Zhao, C., Deng, C., Ruan, C., Dai, D., Chen, D., Ji, D., Li, E., Lin, F., Dai, F., Luo, F., Hao, G., Chen, G., Li, G., Zhang, H., Xu, H....

    work page doi:10.1038/s41586-025-09422-z 2025

  27. [27]

    In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Heilbron, F.C., Escorcia, V., Ghanem, B., Niebles, J.C.: Activitynet: A large-scale video benchmark for human activity understanding. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 961–970 (2015).https: //doi.org/10.1109/CVPR.2015.7298698

    work page doi:10.1109/cvpr.2015.7298698 2015

  28. [28]

    In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Huang, D.A., Ramanathan, V., Mahajan, D., Torresani, L., Paluri, M., Fei-Fei, L., Niebles, J.C.: What makes a video a video: Analyzing temporal information in video understanding models and datasets. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7366–7375 (2018).https://doi. org/10.1109/CVPR.2018.00769

    work page doi:10.1109/cvpr.2018.00769 2018

  29. [29]

    In: The Twelfth International Con- ference on Learning Representations (2024),https://openreview.net/forum?id= zYXFMeHRtO

    Huang, X., Zhou, H., Yao, K., Han, K.: FROSTER: Frozen CLIP is a strong teacher for open-vocabulary action recognition. In: The Twelfth International Con- ference on Learning Representations (2024),https://openreview.net/forum?id= zYXFMeHRtO

    2024

  30. [30]

    Jain, S., Ahmed, U.Z., Sahai, S., Leong, B.: Beyond consensus: Mitigating the agreeableness bias in llm judge evaluations (2025),https://arxiv.org/abs/2510. 11822

    2025

  31. [31]

    In: 2020 IEEE/CVF Confer- enceonComputerVisionandPatternRecognition,CVPR2020,Seattle,WA,USA, June 13-19, 2020

    Ji, J., Krishna, R., Fei-Fei, L., Niebles, J.C.: Action genome: Actions as com- positions of spatio-temporal scene graphs. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10233–10244 (2020). https://doi.org/10.1109/CVPR42600.2020.01025

    work page doi:10.1109/cvpr42600.2020.01025 2020

  32. [32]

    http://crcv.ucf.edu/THUMOS14/(2014)

    Jiang, Y.G., Liu, J., Roshan Zamir, A., Toderici, G., Laptev, I., Shah, M., Suk- thankar,R.:THUMOSchallenge:Actionrecognitionwithalargenumberofclasses. http://crcv.ucf.edu/THUMOS14/(2014)

    2014

  33. [33]

    In: Thirty-seventh Conference on Neural Information Processing Systems (2023),https://openreview.net/forum? id=e2wtjx0Yqu

    Jin, Z., Chen, Y., Leeb, F., Gresele, L., Kamal, O., LYU, Z., Blin, K., Adauto, F.G., Kleiman-Weiner, M., Sachan, M., Schölkopf, B.: CLadder: A benchmark to assess causal reasoning capabilities of language models. In: Thirty-seventh Conference on Neural Information Processing Systems (2023),https://openreview.net/forum? id=e2wtjx0Yqu

    2023

  34. [34]

    In: 2014 IEEE Conference on Computer Vision and Pattern Recognition

    Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1725–1732 (2014). https://doi.org/10.1109/CVPR.2014.223

    work page doi:10.1109/cvpr.2014.223 2014

  35. [35]

    In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T

    Koupaee, M., Bai, X., Chen, M., Durrett, G., Chambers, N., Balasubramanian, N.: Causal graph based event reasoning using semantic relation experts. In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T. (eds.) Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Learning to Deny: Action Denial in Multimodal Lar...

    work page doi:10.18653/v1/2025.acl-long.1269 2025

  36. [36]

    2011 International Conference on Computer Vision pp

    Kuehne, H., Jhuang, H., Garrote, E., Poggio, T.A., Serre, T.: Hmdb: A large video database for human motion recognition. 2011 International Conference on Computer Vision pp. 2556–2563 (2011),https://api.semanticscholar.org/ CorpusID:206769852

    2011

  37. [37]

    ArXivabs/2005.00214 (2020),https://api.semanticscholar.org/CorpusID:218470050

    Li, A., Thotakuri, M., Ross, D.A., Carreira, J., Vostrikov, A., Zisserman, A.: The ava-kinetics localized human actions video dataset. ArXivabs/2005.00214 (2020),https://api.semanticscholar.org/CorpusID:218470050

    arXiv 2005

  38. [38]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Li, B., Ge, Y., Ge, Y., Wang, G., Wang, R., Zhang, R., Shan, Y.: Seed- bench: Benchmarking multimodal large language models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 13299–13308 (June 2024)

    2024

  39. [39]

    In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Li, K., Wang, Y., He, Y., Li, Y., Wang, Y., Liu, Y., Wang, Z., Xu, J., Chen, G., Lou, P., Wang, L., Qiao, Y.: Mvbench: A comprehensive multi-modal video understanding benchmark. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 22195–22206 (2024).https://doi.org/10. 1109/CVPR52733.2024.02095

    arXiv 2024

  40. [40]

    In: Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Is- rael, October 23–27, 2022, Proceedings, Part IV

    Li, T., Foo, L.G., Ke, Q., Rahmani, H., Wang, A., Wang, J., Liu, J.: Dy- namic spatio-temporal specialization learning for fine-grained action recognition. In: Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Is- rael, October 23–27, 2022, Proceedings, Part IV. p. 386–403. Springer-Verlag, Berlin, Heidelberg (2022).https://doi.org/10.1007/...

    work page doi:10.1007/978-3-031-19772-7_23 2022

  41. [41]

    arXiv preprint arXiv:2501.00574 (2024)

    Li, X., Wang, Y., Yu, J., Zeng, X., Zhu, Y., Huang, H., Gao, J., Li, K., He, Y., Wang, C., Qiao, Y., Wang, Y., Wang, L.: Videochat-flash: Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00574 (2024)

    Pith/arXiv arXiv 2024

  42. [42]

    In: Kwok, J

    Li, Y., Yang, X., Bao, B.K., Xu, C.: Graph prompts: Adapting video graph for video question answering. In: Kwok, J. (ed.) Proceedings of the Thirty-Fourth In- ternational Joint Conference on Artificial Intelligence, IJCAI-25. pp. 1485–1493. International Joint Conferences on Artificial Intelligence Organization (8 2025). https://doi.org/10.24963/ijcai.202...

    work page doi:10.24963/ijcai.2025/166 2025

  43. [43]

    In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y

    Li, Y., Li, Y., Vasconcelos, N.: Resound: Towards action recognition without rep- resentation bias. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. pp. 520–535. Springer International Publishing, Cham (2018)

    2018

  44. [44]

    Advances in Neural Information Processing Systems 33(2020)

    Li, Y., Torralba, A., Anandkumar, A., Fox, D., Garg, A.: Causal discovery in physical systems from videos. Advances in Neural Information Processing Systems 33(2020)

    2020

  45. [45]

    In: Proceedings of the Workshop on Visual Analysis in Smart and Connected Communities

    Liu, C., Hu, Y., Li, Y., Song, S., Liu, J.: Pku-mmd: A large scale benchmark for skeleton-based human action understanding. In: Proceedings of the Workshop on Visual Analysis in Smart and Connected Communities. p. 1–8. VSCC ’17, Associ- ation for Computing Machinery, New York, NY, USA (2017).https://doi.org/ 10.1145/3132734.3132739,https://doi.org/10.1145...

    work page doi:10.1145/3132734.3132739 2017

  46. [46]

    IEEE Transactions on Pattern Analysis and Machine Intelligence42(10), 2684–2701 (2020).https: //doi.org/10.1109/TPAMI.2019.2916873 20 R

    Liu,J.,Shahroudy,A.,Perez,M.,Wang,G.,Duan,L.Y.,Kot,A.C.:Nturgb+d120: A large-scale benchmark for 3d human activity understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence42(10), 2684–2701 (2020).https: //doi.org/10.1109/TPAMI.2019.2916873 20 R. Abdullah et al

    work page doi:10.1109/tpami.2019.2916873 2020

  47. [47]

    In: The Thirteenth Inter- national Conference on Learning Representations (2025),https://openreview

    Liu, Z., Dong, Y., Liu, Z., Hu, W., Lu, J., Rao, Y.: Oryx MLLM: On-demand spatial-temporal understanding at arbitrary resolution. In: The Thirteenth Inter- national Conference on Learning Representations (2025),https://openreview. net/forum?id=ODiY6pbHZQ

    2025

  48. [48]

    arXiv:2405.20797 (2024)

    Lu, S., Li, Y., Chen, Q.G., Xu, Z., Luo, W., Zhang, K., Ye, H.J.: Ovis: Struc- tural embedding alignment for multimodal large language model. arXiv:2405.20797 (2024)

    arXiv 2024

  49. [49]

    arXiv:2508.11737 (2025)

    Lu, S., Li, Y., Xia, Y., Hu, Y., Zhao, S., Ma, Y., Wei, Z., Li, Y., Duan, L., Zhao, J., Han, Y., Li, H., Chen, W., Tang, J., Hou, C., Du, Z., Zhou, T., Zhang, W., Ding, H., Li, J., Li, W., Hu, G., Gu, Y., Yang, S., Wang, J., Sun, H., Wang, Y., Sun, H., Huang, J., He, Y., Shi, S., Zhang, W., Zheng, G., Jiang, J., Gao, S., Wu, Y.F., Chen, S., Chen, Y., Chen...

    Pith/arXiv arXiv 2025

  50. [50]

    Ma, H., Pathak, V., Wang, D.Z.: Bridging vision language models and symbolic grounding for video question answering (2025),https://arxiv.org/abs/2509. 11862

    2025

  51. [51]

    In: Arai, K

    Malmqvist, L.: Sycophancy in large language models: Causes and mitigations. In: Arai, K. (ed.) Intelligent Computing. pp. 61–74. Springer Nature Switzerland, Cham (2025)

    2025

  52. [52]

    McDuff, D., Song, Y., Lee, J., Vineet, V., Vemprala, S., Gyde, N., Salman, H., Ma, S., Sohn, K., Kapoor, A.: Causalcity: Complex simulations with agency for causal discovery and reasoning (June 2021),https://www.microsoft.com/en-us/ research/publication/causalcity- complex- simulations- with- agency- for- causal-discovery-and-reasoning/, preprint under review

    2021

  53. [53]

    In: 2019 IEEE/CVF International Conference on Computer Vi- sion (ICCV)

    Miech, A., Zhukov, D., Alayrac, J.B., Tapaswi, M., Laptev, I., Sivic, J.: Howto100m: Learning a text-video embedding by watching hundred million nar- rated video clips. In: 2019 IEEE/CVF International Conference on Computer Vi- sion (ICCV). pp. 2630–2640 (2019).https://doi.org/10.1109/ICCV.2019.00272

    work page doi:10.1109/iccv.2019.00272 2019

  54. [54]

    IEEE Transactions on Pattern Analysis & Machine Intelligence42(02), 502–508 (Feb 2020).https: //doi.org/10.1109/TPAMI.2019.2901464,https://doi.ieeecomputersociety

    Monfort, M., Vondrick, C., Oliva, A., Andonian, A., Zhou, B., Ramakrishnan, K., Bargal, S.A., Yan, T., Brown, L., Fan, Q., Gutfreund, D.: Moments in Time Dataset: One Million Videos for Event Understanding . IEEE Transactions on Pattern Analysis & Machine Intelligence42(02), 502–508 (Feb 2020).https: //doi.org/10.1109/TPAMI.2019.2901464,https://doi.ieeeco...

    work page doi:10.1109/tpami.2019.2901464 2020

  55. [55]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Nguyen, T.T., Nguyen, P., Cothren, J., Yilmaz, A., Luu, K.: Hyperglm: Hyper- graph for video scene graph generation and anticipation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 29150–29160 (June 2025)

    2025

  56. [56]

    In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T

    Ni, B., Peng, H., Chen, M., Zhang, S., Meng, G., Fu, J., Xiang, S., Ling, H.: Expanding language-image pretrained models for general video recognition. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022. pp. 1–18. Springer Nature Switzerland, Cham (2022)

    2022

  57. [57]

    OpenAI: Gpt-4o mini: Advancing cost-efficient intelligence.https://openai.com/ index/gpt-4o-mini-advancing-cost-efficient-intelligence/(Jul 2024), ac- cessed 2026-06-27

    2024

  58. [58]

    https://openai.com/index/sycophancy-in-gpt-4o/(Apr 2025), accessed 2025- 11-09

    OpenAI: Sycophancy in gpt-4o: what happened and what we’re doing about it. https://openai.com/index/sycophancy-in-gpt-4o/(Apr 2025), accessed 2025- 11-09

    2025

  59. [59]

    co / prithivMLmods / LumianâĂŚVLRâĂŚ7BâĂŚThinking(2025), hugging Face model card Learning to Deny: Action Denial in Multimodal Large Language Models 21

    prithivMLmods: Lumian-vlr-7b-thinking.https : / / huggingface . co / prithivMLmods / LumianâĂŚVLRâĂŚ7BâĂŚThinking(2025), hugging Face model card Learning to Deny: Action Denial in Multimodal Large Language Models 21

    2025

  60. [60]

    In: The Thirteenth International Confer- ence on Learning Representations (2025),https://openreview.net/forum?id= OxKi02I29I

    Ranasinghe, K., Li, X., Kahatapitiya, K., Ryoo, M.S.: Understanding long videos with multimodal language models. In: The Thirteenth International Confer- ence on Learning Representations (2025),https://openreview.net/forum?id= OxKi02I29I

    2025

  61. [61]

    6059–6069

    Rasheed, H., Khattak, M.U., Maaz, M., Khan, S., Khan, F.S.: Fine-tuned clip models are efficient video learners. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6545–6554 (2023).https://doi. org/10.1109/CVPR52729.2023.00633

    work page doi:10.1109/cvpr52729.2023.00633 2023

  62. [62]

    In: Ku, L.W., Martins, A., Srikumar, V

    Rrv, A., Tyagi, N., Uddin, M.N., Varshney, N., Baral, C.: Chaos with keywords: Exposing large language models sycophancy to misleading keywords and evaluat- ing defense strategies. In: Ku, L.W., Martins, A., Srikumar, V. (eds.) Findings of the Association for Computational Linguistics: ACL 2024. pp. 12717–12733. As- sociation for Computational Linguistics...

    work page doi:10.18653/v1/2024.findings-acl.755 2024

  63. [63]

    In: 2020 IEEE/CVF Confer- enceonComputerVisionandPatternRecognition,CVPR2020,Seattle,WA,USA, June 13-19, 2020

    Shao, D., Zhao, Y., Dai, B., Lin, D.: Finegym: A hierarchical video dataset for fine-grained action understanding. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2613–2622 (2020).https://doi. org/10.1109/CVPR42600.2020.00269

    work page doi:10.1109/cvpr42600.2020.00269 2020

  64. [64]

    In: The Twelfth Interna- tional Conference on Learning Representations (2024),https://openreview.net/ forum?id=tvhaxkMKAn

    Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S.R., DURMUS, E., Hatfield-Dodds, Z., Johnston, S.R., Kravec, S.M., Maxwell, T., Mc- Candlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., Perez, E.: Towards understanding sycophancy in language models. In: The Twelfth Interna- tional Conference on Learning Representati...

    2024

  65. [65]

    In: Leibe, B., Matas, J., Sebe, N., Welling, M

    Sigurdsson, G.A., Varol, G., Wang, X., Farhadi, A., Laptev, I., Gupta, A.: Hol- lywood in homes: Crowdsourcing data collection for activity understanding. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. pp. 510–526. Springer International Publishing, Cham (2016)

    2016

  66. [66]

    In: CRCV-TR-12-01 (2012)

    Soomro, K., Roshan Zamir, A., Shah, M.: UCF101: A dataset of 101 human actions classes from videos in the wild. In: CRCV-TR-12-01 (2012)

    2012

  67. [67]

    CoRRabs/2506.08553(June 2025),https://doi.org/ 10.48550/arXiv.2506.08553

    Taluzzi, A., Gesualdi, D., Santambrogio, R., Plizzari, C., Palermo, F., Mentasti, S., Matteucci, M.: From pixels to graphs: using scene and knowledge graphs for hd-epic vqa challenge. CoRRabs/2506.08553(June 2025),https://doi.org/ 10.48550/arXiv.2506.08553

    work page doi:10.48550/arxiv.2506.08553 2025

  68. [68]

    In: Proceedings of the 29th ACM International Conference on Multimedia

    Tang, M., Wang, Z., LIU, Z., Rao, F., Li, D., Li, X.: Clip4caption: Clip for video caption. In: Proceedings of the 29th ACM International Conference on Multimedia. p. 4858–4862. MM ’21, Association for Computing Machinery, New York, NY, USA (2021).https://doi.org/10.1145/3474085.3479207,https://doi.org/ 10.1145/3474085.3479207

    work page doi:10.1145/3474085.3479207 2021

  69. [69]

    Abdullah et al

    Team, K., Du, A., Yin, B., Xing, B., Qu, B., Wang, B., Chen, C., Zhang, C., Du, C., Wei, C., Wang, C., Zhang, D., Du, D., Wang, D., Yuan, E., Lu, E., Li, F., Sung, F., Wei, G., Lai, G., Zhu, H., Ding, H., Hu, H., Yang, H., Zhang, H., Wu, H., Yao, H., Lu, H., Wang, H., Gao, H., Zheng, H., Li, J., Su, J., Wang, J., Deng, J., Qiu, J., Xie, J., Wang, J., Liu,...

    Pith/arXiv arXiv 2025

  70. [70]

    In: Causality and Large Models @NeurIPS 2024 (2024),https://openreview

    Vashishtha, A., Kumar, A., Pandey, A., Reddy, A.G., Balasubramanian, V.N., Sharma, A.: Teaching transformers causal reasoning through axiomatic training. In: Causality and Large Models @NeurIPS 2024 (2024),https://openreview. net/forum?id=vnFtU3fO9h

    2024

  71. [71]

    IEEE Transactions on Neural Networks and Learning Systems36(1), 625–637 (2025).https://doi.org/10

    Wang, M., Xing, J., Mei, J., Liu, Y., Jiang, Y.: Actionclip: Adapting language- image pretrained models for video action recognition. IEEE Transactions on Neural Networks and Learning Systems36(1), 625–637 (2025).https://doi.org/10. 1109/TNNLS.2023.3331841

    arXiv 2025

  72. [72]

    arXiv preprint arXiv:2501.12386 (2025)

    Wang, Y., Li, X., Yan, Z., He, Y., Yu, J., Zeng, X., Wang, C., Ma, C., Huang, H., Gao, J., Dou, M., Chen, K., Wang, W., Qiao, Y., Wang, Y., Wang, L.: In- ternvideo2.5: Empowering video mllms with long and rich context modeling. arXiv preprint arXiv:2501.12386 (2025)

    Pith/arXiv arXiv 2025

  73. [73]

    International Journal of Computer Vision129, 1675 – 1690 (2019), https://api.semanticscholar.org/CorpusID:209376248

    Weinzaepfel, P., Rogez, G.: Mimetics: Towards understanding human actions out of context. International Journal of Computer Vision129, 1675 – 1690 (2019), https://api.semanticscholar.org/CorpusID:209376248

    2019

  74. [74]

    In: ICML (2023)

    Weng, Z., Yang, X., Li, A., Wu, Z., Jiang, Y.G.: Open-vclip: Transforming clip to an open-vocabulary video model via interpolated weight optimization. In: ICML (2023)

    2023

  75. [75]

    arXiv preprint arXiv:2501.05901 (2025)

    Wu, Z., Chen, Z., Luo, R., Zhang, C., Gao, Y., He, Z., Wang, X., Lin, H., Qiu, M.: Valley2: Exploring multimodal models with scalable vision-language design. arXiv preprint arXiv:2501.05901 (2025)

    arXiv 2025

  76. [76]

    In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

    Xiang, W., Li, C., Li, K., Wang, B., Hua, X.S., Zhang, L.: Cdad: A common daily action dataset with collected hard negative samples. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 3920–3929 (2022).https://doi.org/10.1109/CVPRW56347.2022.00437

    work page doi:10.1109/cvprw56347.2022.00437 2022

  77. [77]

    Xu, H., Ghosh, G., Huang, P.Y., Okhonko, D., Aghajanyan, A., Metze, F., Zettle- moyer, L., Feichtenhofer, C.: Videoclip: Contrastive pre-training for zero-shot video-text understanding (2021)

    2021

  78. [78]

    In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

    Yao, B., Fei-Fei, L.: Grouplet: A structured image representation for recognizing human and object interactions. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 9–16 (2010).https://doi.org/ 10.1109/CVPR.2010.5540234

    work page doi:10.1109/cvpr.2010.5540234 2010

  79. [79]

    Yeung, S., Russakovsky, O., Jin, N., Andriluka, M., Mori, G., Fei-Fei, L.: Every moment counts: Dense detailed labeling of actions in complex videos. Int. J. Com- put. Vision126(2–4), 375–389 (Apr 2018).https://doi.org/10.1007/s11263- 017-1013-y,https://doi.org/10.1007/s11263-017-1013-y

    work page doi:10.1007/s11263- 2018

  80. [80]

    In: ICLR (2020)

    Yi, K., Gan, C., Li, Y., Kohli, P., Wu, J., Torralba, A., Tenenbaum, J.B.: CLEVRER: collision events for video representation and reasoning. In: ICLR (2020)

    2020

Showing first 80 references.