--- title: "Who cites LVBench: An Extreme Long Video — 60 citing papers" description: "60 papers of the Elamur Research library cite «LVBench: An Extreme Long Video Understanding Benchmark», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/wang-2024-lvbench-extreme-long-video-understanding/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2024-lvbench-extreme-long-video-understanding/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LVBench: An Extreme Long Video Understanding…](https://md.research.elamur.ai/free/en/papers/wang-2024-lvbench-extreme-long-video-understanding/) / cited by # Who cites «LVBench: An Extreme Long Video Understanding Benchmark» 60 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← back to «LVBench: An Extreme Long Video Understanding Benchmark»](https://md.research.elamur.ai/free/en/papers/wang-2024-lvbench-extreme-long-video-understanding/) 1. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/) 2. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/) 3. **[Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://md.research.elamur.ai/free/en/papers/fu-2024-video-mme-first-ever-comprehensive/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Chaoyou Fu, Yuhan Dai, Yongdong Luo, et al.*2024 · 427 citations* 4. [**Qwen3-VL Technical Report arXiv:2511.21631**Shuai Bai, Yuxuan Cai, Ruizhe Chen, et al.*2025 · 370 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/) 5. [**InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency arXiv:2508.18265**Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/) 6. [**Qwen3-Omni Technical Report arXiv:2509.17765**Jin Xu, Zhifang Guo, Hangrui Hu, et al.*2025 · 117 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen3-omni-technical-report/) 7. [**GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning arXiv:2507.01006**GLM-V Team, Wenyi Hong, Wenmeng Yu, et al.*2025 · 89 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-v-glm-4-5v-glm/) 8. [**Long Context Transfer from Language to Vision arXiv:2406.16852**Peiyuan Zhang, Kaichen Zhang, Bo Li, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-long-context-transfer-language-vision/) 9. [**Seed1.5-VL Technical Report arXiv:2505.07062**Dong Guo, Faming Wu, Feida Zhu, et al.*2025 · 67 citations*](https://md.research.elamur.ai/free/en/papers/guo-2025-seed1-5-vl-technical/) 10. [**Kimi K2.5: Visual Agentic Intelligence arXiv:2602.02276**Kimi Team, Tongtong Bai, Yifan Bai, et al.*2026 · 66 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2026-kimi-k2-5-visual/) 11. [**CogVLM2: Visual Language Models for Image and Video Understanding arXiv:2408.16500**Wenyi Hong, Weihan Wang, Ming Ding, et al.*2024 · 52 citations*](https://md.research.elamur.ai/free/en/papers/hong-2024-cogvlm2-visual-language-models/) 12. [**Qwen3.5-Omni Technical Report arXiv:2604.15804**Qwen Team*2026 · 29 citations*](https://md.research.elamur.ai/free/en/papers/qwen-team-2026-qwen3-5-omni-technical/) 13. [**MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe arXiv:2509.18154**Tianyu Yu, Zefan Wang, Chongyi Wang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-minicpm-v-4-5/) 14. **[VideoPro: Adaptive Program Reasoning for Long Video Understanding](https://md.research.elamur.ai/free/en/papers/li-2026-videopro-adaptive-program-reasoning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenglin Li, Feng Han, Yikun Wang, et al.*2026 · 6 citations* 15. [**MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction arXiv:2604.27393**Junbo Cui, Bokai Xu, Chongyi Wang, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/cui-2026-minicpm-o-4-5/) 16. **[UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities](https://md.research.elamur.ai/free/en/papers/yeo-2026-universalrag-retrieval-augmented-generation-over/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, et al.*2026 · 4 citations* 17. **[Agentic Very Long Video Understanding](https://md.research.elamur.ai/free/en/papers/rege-2026-agentic-very-long-video-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aniket Rege, Arka Sadhu, Yuliang Li, et al.*2026 · 1 citation* 18. **[A Survey of Large Models in Sports](https://md.research.elamur.ai/free/en/papers/xu-2026-survey-large-models-sports/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yichen Xu, Jianzhe Ma, Chuhan Wang, et al.*2026* 19. **[Minimal Clips, Maximum Salience: Long Video Summarization via Key Moment Extraction](https://md.research.elamur.ai/free/en/papers/pennec-2026-minimal-clips-maximum-salience-long/) [International Workshop on Spoken Dialogue System Technology](https://md.research.elamur.ai/free/en/venues/iwsds/)**Galann Pennec, Zhengyuan Liu, Nicholas Asher, et al.*2026* 20. **[MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues](https://md.research.elamur.ai/free/en/papers/pan-2026-mt-video-bench-holistic-video/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yaning Pan, Qianqian Xie, Guohui Zhang, et al.*2026* 21. **[MVP: Enhancing Video Large Language Models via Self-supervised Masked Video Prediction](https://md.research.elamur.ai/free/en/papers/sun-2026-mvp-enhancing-video-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaokun Sun, Zezhong Wu, Zewen Ding, et al.*2026* 22. [**Qwen3.8-Omni: Towards Native Omni-Modal Agents arXiv:2609.25611**Qwen Team*2026*](https://md.research.elamur.ai/free/en/papers/qwen-team-2026-qwen3-8-omni-native/) 23. **[SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series](https://md.research.elamur.ai/free/en/papers/pennec-2026-sagaqa-multi-hop-reasoning-benchmark/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Galann Pennec, Zhengyuan Liu, Nicholas Asher, et al.*2026* 24. **[v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound](https://md.research.elamur.ai/free/en/papers/shi-2026-v-hub-benchmark-video-humor/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, et al.*2026* 36 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/wang-2024-lvbench-extreme-long-video-understanding/) — LVBench: An Extreme Long Video Understanding Benchmark