next-token prediction for large multimodal
models.
Nature, 650(8101):327–333, 2026.
doi: 10.1038/s41586-025-10041-x.
URL https://www.nature.com/articles/s41586-025-10041-x.
Wang et al. (2024)
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo,
Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai
Wang, Alex Zhuang, Rongqi Fan, et al.
MMLU-Pro: A more robust and challenging multi-task language
understanding benchmark.
In Advances in Neural Information Processing Systems (NeurIPS),
Datasets and Benchmarks Track, pp. 95266–95290, 2024.
doi: 10.52202/079017-3018.
URL
https://proceedings.neurips.cc/paper_files/paper/2024/hash/ad236edc564f3e3156e1b2feafb99a24-Abstract-Datasets_and_Benchmarks_Track.html.
Wastl et al. (2026)
Michelle Wastl, Jannis Vamvas, and Rico Sennrich.
SwissGov-RSD: A human-annotated, cross-lingual benchmark for
token-level recognition of semantic differences between related documents.
In Proceedings of the Annual Meeting of the Association for
Computational Linguistics (ACL), pp. 31134–31163, 2026.
doi: 10.18653/v1/2026.acl-long.1437.
URL https://aclanthology.org/2026.acl-long.1437/.
Weck et al. (2024)
Benno Weck, Ilaria Manco, Emmanouil Benetos, Elio Quinton, George Fazekas, and
Dmitry Bogdanov.
MuChoMusic: Evaluating music understanding in multimodal
audio-language models.
In Proceedings of the International Society for Music
Information Retrieval Conference (ISMIR), pp. 825–833, 2024.
doi: 10.5281/zenodo.14877459.
URL https://doi.org/10.5281/zenodo.14877459.
Wei et al. (2025)
Lai Wei, Yuting Li, Kaipeng Zheng, Chen Wang, Yue Wang, Linghe Kong, Lichao
Sun, and Weiran Huang.
Advancing multimodal reasoning via reinforcement learning with cold
start, 2025.
URL https://arxiv.org/abs/2505.22334.
Wen et al. (2026)
Zichen Wen, Boxue Yang, Shuang Chen, Yaojie Zhang, Yuhang Han, Junlong Ke, Cong
Wang, Yicheng Fu, Jiawang Zhao, Jiangchao Yao, Xi Fang, Zhen Wang, Henxing
Cai, Lin Yao, Zhifeng Gao, et al.
Innovator-VL: A multimodal large language model for scientific
discovery, 2026.
URL https://arxiv.org/abs/2601.19325.
Wiedmann et al. (2025)
Luis Wiedmann, Orr Zohar, Amir Mahla, Xiaohan Wang, Rui Li, Thibaud Frere,
Leandro von Werra, Aritra Roy Gosthipaty, and Andrés Marafioti.
FineVision: Open data is all you need, 2025.
URL https://arxiv.org/abs/2510.17269.
Wu et al. (2023)
Yijing Wu, SaiKrishna Rallabandi, Ravisutha Srinivasamurthy, Parag Pravin
Dakle, Alolika Gon, and Preethi Raghavan.
HeySQuAD: A spoken question answering dataset, 2023.
URL https://arxiv.org/abs/2304.13689.
Wuhrmann et al. (2026)
Arthur Wuhrmann, Gaetan Stein, Daniel Brunner, and Andrei Kucharavy.
Measuring & mitigating over-alignment for LLMs in multilingual
criminal law courts, 2026.
URL https://arxiv.org/abs/2606.23375.
Xie & Wu (2024)
Zhifei Xie and Changqiao Wu.
Mini-Omni: Language models can hear, talk while thinking in
streaming, 2024.
URL https://arxiv.org/abs/2408.16725.
Xiong et al. (2025)
Zheyang Xiong, Vasilis Papageorgiou, Kangwook Lee, and Dimitris Papailiopoulos.
From artificial needles to real haystacks: Improving retrieval
capabilities in LLMs by finetuning on synthetic data.
In International Conference on Learning Representations
(ICLR), pp. 7276–7291, 2025.
URL
https://proceedings.iclr.cc/paper_files/paper/2025/hash/15a321fbfc19fce9b325ec6e77dfb597-Abstract-Conference.html.
Xu et al. (2025)
Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen,
Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, and
Junyang Lin.
Qwen2.5-Omni technical report, 2025.
URL https://arxiv.org/abs/2503.20215.
Xu (2025)
Yixuan Xu.
Quantifying training data retention in large language models: An
analysis of pretraining factors and mitigation strategies.
Master’s thesis, EPFL, 2025.
URL https://infoscience.epfl.ch/handle/20.500.14299/253615.
Xu et al. (2026)
Zhangchen Xu, Adriana Meza Soria, Shawn Tan, Anurag Roy, Ashish Sunil Agrawal,
Radha Poovendran, and Rameswar Panda