[48] Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin,
     Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: Benchmarking
     multi-task long video understanding, 2025. URL https://arxiv.org/abs/2406.04264.
[49] Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for
     long-context interleaved video-language understanding, 2024. URL https://arxiv.org/
     abs/2407.15754.
[50] Chao Deng, Jiale Yuan, Pi Bu, Peijie Wang, Zhong-Zhi Li, Jian Xu, Xiao-Hui Li, Yuan Gao, Jun
     Song, Bo Zheng, et al. Longdocurl: a comprehensive multimodal long document benchmark
     integrating understanding, reasoning, and locating. arXiv preprint arXiv:2412.18424, 2024.
[51] Yew Ken Chia, Liying Cheng, Hou Pong Chan, CHAOQUN LIU, Maojia Song, Mahani
     Aljunied, Soujanya Poria, and Lidong Bing. M-longdoc: A benchmark for multimodal
     super-long document understanding and a retrieval-aware tuning framework. 2024.
[52] Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman
     Pan, Yangqiu Song, and Dong Yu. Divscene: Towards open-vocabulary object navigation with
     large vision language models in diverse scenes. arXiv preprint arXiv:2410.02730, 2024.
[53] Dingjie Song, Shunian Chen, Guiming Hardy Chen, Fei Yu, Xiang Wan, and Benyou Wang.
     Milebench: Benchmarking mllms in long context. arXiv preprint arXiv:2404.18532, 2024.
[54] Xidong Wang, Dingjie Song, Shunian Chen, Chen Zhang, and Benyou Wang. Longllava:
     Scaling multi-modal llms to 1000 images efficiently via a hybrid architecture. arXiv preprint
     arXiv:2409.02889, 2024.
[55] Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and
     Jingren Zhou. mplug-owl3: Towards long image-sequence understanding in multi-modal large
     language models. CoRR, 2024.
[56] Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang, Baojun Wang, Wanjun Zhong, Zezhong
     Wang, and Kam-Fai Wong. Perltqa: A personal long-term memory dataset for memory
     classification, retrieval, and synthesis in question answering, 2024. URL https://arxiv.or
     g/abs/2402.16288.
[57] Rongwu Xu, Zehan Qi, Zhijiang Guo, Cunxiang Wang, Hongru Wang, Yue Zhang, and Wei
     Xu. Knowledge conflicts for LLMs: A survey. In Proceedings of the 2024 Conference on
     Empirical Methods in Natural Language Processing, pages 8541–8565, 2024.
[58] Hanning Zhang, Shizhe Diao, Yong Lin, Yi R. Fung, Qing Lian, Xingyao Wang, Yangyi Chen,
     Heng Ji, and Tong Zhang. R-tuning: Instructing large language models to say “I don’t know”.
     In Proceedings of the 2024 Conference of the North American Chapter of the Association for
     Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages
     7113–7139, 2024.
[59] Google DeepMind. Gemini 3 Pro Model Card. https://deepmind.google/models/mod
     el-cards/gemini-3-pro/, 2026.
[60] Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton
     Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander Rush, Douwe Kiela, et al. Obelics:
     An open web-scale filtered dataset of interleaved image-text documents. Advances in Neural
     Information Processing Systems, 36:71683–71702, 2023.
[61] Hexiang Hu, Yi Luan, Yang Chen, Urvashi Khandelwal, Mandar Joshi, Kenton Lee, Kristina
     Toutanova, and Ming-Wei Chang. Open-domain visual entity recognition: Towards recognizing
     millions of wikipedia entities. In Proceedings of the IEEE/CVF International Conference on
     Computer Vision, pages 12065–12075, 2023.
[62] Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi,
     Xin Liu, and Yangqiu Song. Abspyramid: Benchmarking the abstraction ability of language
     models with a unified entailment graph. In Findings of the Association for Computational
     Linguistics: NAACL 2024, pages 3991–4010, 2024.
[63] Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin
     Liu, Yangqiu Song, Ginny Wong, and Simon See. Absinstruct: Eliciting abstraction ability
     from llms through explanation tuning with plausibility estimation. In Proceedings of the 62nd
     Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),
     pages 973–994, 2024.


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