ao Yang, Chen Wei, Oscar Qian, Hui En Pang, Xuanke Shi,
Kewang Deng, Xiaoyang Han, Zukai Chen, et al.
Holistic evaluation of multimodal LLMs on spatial intelligence,
2025.
URL https://arxiv.org/abs/2508.13142.
Cai et al. (2026)
Zhongang Cai, Ruisi Wang, Chenyang Gu, Fanyi Pu, Junxiang Xu, Yubo Wang, Wanqi
Yin, Zhitao Yang, Chen Wei, Tongxi Zhou, Qingping Sun, Hui En Pang, Jiaqi Li,
Oscar Qian, Zhiqian Lin, et al.
Scaling spatial intelligence with multimodal foundation models.
In Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR), pp. 7879–7890, 2026.
URL
https://openaccess.thecvf.com/content/CVPR2026/html/Cai_Scaling_Spatial_Intelligence_with_Multimodal_Foundation_Models_CVPR_2026_paper.html.
Chameleon
Team (2024)
Chameleon Team.
Chameleon: Mixed-modal early-fusion foundation models, 2024.
URL https://arxiv.org/abs/2405.09818.
Chen et al. (2025a)
Hardy Chen, Haoqin Tu, Fali Wang, Hui Liu, Xianfeng Tang, Xinya Du, Yuyin Zhou,
and Cihang Xie.
SFT or RL? an early investigation into training R1-Like
reasoning large vision-language models.
Transactions on Machine Learning Research, 2025a.
URL https://openreview.net/forum?id=wZI5qkQeDF.
Chen et al. (2025b)
Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang,
and Benyou Wang.
Towards medical complex reasoning with LLMs through medical
verifiable problems.
In Findings of the Association for Computational Linguistics:
ACL, pp. 14552–14573, 2025b.
doi: 10.18653/v1/2025.findings-acl.751.
URL https://aclanthology.org/2025.findings-acl.751/.
Chen et al. (2025c)
Junying Chen, Zhenyang Cai, Zhiheng Liu, Yunjin Yang, Rongsheng Wang, Qingying
Xiao, Xiangyi Feng, Zhan Su, Jing Guo, Xiang Wan, Guangjun Yu, Haizhou Li,
and Benyou Wang.
ShizhenGPT: Towards multimodal LLMs for traditional Chinese
medicine, 2025c.
URL https://arxiv.org/abs/2508.14706.
Chiu et al. (2025)
Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li,
Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz,
and Yejin Choi.
CulturalBench: A robust, diverse and challenging benchmark for
measuring LMs’ cultural knowledge through human-AI red-teaming.
In Proceedings of the Annual Meeting of the Association for
Computational Linguistics (ACL), pp. 25663–25701, 2025.
doi: 10.18653/v1/2025.acl-long.1247.
URL https://aclanthology.org/2025.acl-long.1247/.
Chu et al. (2024)
Yunfei Chu, Jin Xu, Qian Yang, Haojie Wei, Xipin Wei, Zhifang Guo, Yichong
Leng, Yuanjun Lv, Jinzheng He, Junyang Lin, Chang Zhou, and Jingren Zhou.
Qwen2-Audio technical report, 2024.
URL https://arxiv.org/abs/2407.10759.
Clark et al. (2026a)
Christopher Clark, Yue Yang, Jae Sung Park, Zixian Ma, Jieyu Zhang, Rohun
Tripathi, Mohammadreza Salehi, Sangho Lee, Taira Anderson, Winson Han, and
Ranjay Krishna.
MolmoPoint: Better pointing for VLMs with grounding tokens.
In Proceedings of the European Conference on Computer Vision
(ECCV), pp. 665–684, 2026a.
doi: 10.1007/978-3-032-37242-0_37.
URL
https://link.springer.com/chapter/10.1007/978-3-032-37242-0_37.
Clark et al. (2026b)
Christopher Clark, Jieyu Zhang, Zixian Ma, Jae Sung Park, Rohun Tripathi,
Sangho Lee, Mohammadreza Salehi, Jason Ren, Chris Dongjoo Kim, Yinuo Yang,
Vincent Shao, Yue Yang, Weikai Huang, Ziqi Gao, Taira Anderson, et al.
Molmo2: Open weights and data for vision-language models with video
understanding and grounding.
In Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR), pp. 28652–28668, 2026b.
URL
https://openaccess.thecvf.com/content/CVPR2026/html/Clark_Molmo2_Open_Weights_and_Data_for_Vision-Language_Models_with_Video_CVPR_2026_paper.html.
Clark et al. (2018)
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa
Schoenick, and Oyvind Tafjord.
Think you have solved question answering? try ARC, the AI2
reasoning challenge, 2018.
URL https://arxiv.org/abs/1803.05457.
Cloud et al. (2026)
Alex Cloud, Minh Le, James Chua, Jan Betley, Ann