 He, Y. Li, Y. Wang, Y. Liu, Z. Wang, J. Xu, G. Chen, P. Luo, et al.
Mvbench: a comprehensive multi-modal video understanding benchmark.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
pp. 22195–22206.
Cited by: §2.1,
Table 1.
Liu et al. (2025)
H. Liu, W. Yan, M. Zaharia, and P. Abbeel
World model on million-length video and language with blockwise ringattention.
International Conference on Learning Representations.
Cited by: §2.2.
Liu et al. (2024)
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang
Lost in the middle: how language models use long contexts.
Transactions of the Association for Computational Linguistics 12, pp. 157–173.
Cited by: §3.1.
Mangalam et al. (2023)
K. Mangalam, R. Akshulakov, and J. Malik
Egoschema: a diagnostic benchmark for very long-form video language understanding.
Advances in Neural Information Processing Systems 36, pp. 46212–46244.
Cited by: §2.1,
Table 1.
Mirzadeh et al. (2024)
I. Mirzadeh, K. Alizadeh, H. Shahrokhi, O. Tuzel, S. Bengio, and M. Farajtabar
Gsm-symbolic: understanding the limitations of mathematical reasoning in large language models.
arXiv preprint arXiv:2410.05229.
Cited by: §1.
Motamed et al. (2025)
S. Motamed, L. Culp, K. Swersky, P. Jaini, and R. Geirhos
Do generative video models learn physical principles from watching videos?.
arXiv preprint arXiv:2501.09038.
Cited by: §1,
§2.2.
OpenAI (2024a)
OpenAI
ChatGPT-4o (aug 6 version).
Note: Large language model
External Links: Link
Cited by: Appendix C,
§4.1.
OpenAI (2024b)
OpenAI
ChatGPT-4o-mini (july 18 version).
Note: Large language model
External Links: Link
Cited by: §4.1.
Shen et al. (2024)
X. Shen, Y. Xiong, C. Zhao, L. Wu, J. Chen, C. Zhu, Z. Liu, F. Xiao, B. Varadarajan, F. Bordes, et al.
Longvu: spatiotemporal adaptive compression for long video-language understanding.
arXiv preprint arXiv:2410.17434.
Cited by: §4.1.
Singh et al. (2024)
A. K. Singh, M. Y. Kocyigit, A. Poulton, D. Esiobu, M. Lomeli, G. Szilvasy, and D. Hupkes
Evaluation data contamination in llms: how do we measure it and (when) does it matter?.
arXiv preprint arXiv:2411.03923.
Cited by: §1.
Srivastava et al. (2022)
A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, et al.
Beyond the imitation game: quantifying and extrapolating the capabilities of language models.
arXiv preprint arXiv:2206.04615.
Cited by: §1.
Sun et al. (2022)
Y. Sun, Q. Chao, Y. Ji, and B. Li
Synopses of movie narratives: a video-language dataset for story understanding.
arXiv preprint arXiv:2203.05711.
Cited by: Appendix A,
§3.
Sun et al. (2024)
Y. Sun, Q. Chao, and B. Li
Event causality is key to computational story understanding.
In The Annual Conference of the North American Chapter of the Association for Computational Linguistics,
External Links: Link
Cited by: §1,
§3.1.
Team et al. (2024)
G. Team, P. Georgiev, V. I. Lei, R. Burnell, L. Bai, A. Gulati, G. Tanzer, D. Vincent, Z. Pan, S. Wang, et al.
Gemini 1.5: unlocking multimodal understanding across millions of tokens of context.
arXiv preprint arXiv:2403.05530.
Cited by: Appendix C,
§4.1.
Vodrahalli et al. (2024)
K. Vodrahalli, S. Ontanon, N. Tripuraneni, K. Xu, S. Jain, R. Shivanna, J. Hui, N. Dikkala, M. Kazemi, B. Fatemi, et al.
Michelangelo: long context evaluations beyond haystacks via latent structure queries.
arXiv preprint arXiv:2409.12640.
Cited by: §1.
Wang et al. (2024a)
W. Wang, Z. He, W. Hong, Y. Cheng, X. Zhang, J. Qi, X. Gu, S. Huang, B. Xu, Y. Dong, et al.
Lvbench: an extreme long video understanding benchmark.
arXiv preprint arXiv:2406.08035.
Cited by: Table 1.
Wang et al. (2024b)
W. Wang, S. Zhang, Y. Ren, Y. Duan, T. Li, S. Liu, M. Hu, Z. Chen, K. Zhang, L. Lu, et al.
Needle in a multimodal haystack.
arXiv preprint arXiv:2406.07230.
Cited by: §1,
§2.1.
West et al. (2023)
P. West, X. Lu, N. Dziri, F. Brahman, L. Li, J. D. Hwang, L. Jiang, J. Fisher, A. Ravichander, K