VTCBench: Can Vision-Language Models Understand Long Contexts with Vision-Text Compression?

@article{hongbo2026,
  title = {VTCBench: Can Vision-Language Models Understand Long Contexts with Vision-Text Compression?},
  author = {Zhao, Hongbo and Wang, Meng and Zhu, Fei and Liu, Wenzhuo and Ni, Bolin and Zeng, Fanhu and Meng, Gaofeng and Zhang, Zhaoxiang and Liu, Cheng-Lin},
  journal = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year = {2026},
  month = {Jan},
}

Zhao, Hongbo et al. (2026). VTCBench: Can Vision-Language Models Understand Long Contexts with Vision-Text Compression?. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing.

Zhao, Hongbo, et al.. "VTCBench: Can Vision-Language Models Understand Long Contexts with Vision-Text Compression?." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026.

Zhao, Hongbo, et al.. "VTCBench: Can Vision-Language Models Understand Long Contexts with Vision-Text Compression?." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (2026).

Zhao, Hongbo et al., 2026. VTCBench: Can Vision-Language Models Understand Long Contexts with Vision-Text Compression?. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing.

TY  - JOUR
TI  - VTCBench: Can Vision-Language Models Understand Long Contexts with Vision-Text Compression?
AU  - Zhao, Hongbo
AU  - Wang, Meng
AU  - Zhu, Fei
AU  - Liu, Wenzhuo
AU  - Ni, Bolin
AU  - Zeng, Fanhu
AU  - Meng, Gaofeng
AU  - Zhang, Zhaoxiang
AU  - Liu, Cheng-Lin
PY  - 2026
JO  - Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
ER  -