State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models

@article{mingxu2026,
  title = {State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models},
  author = {Chai, Mingxu and Liu, Chenyu and Shen, Ziyu and Zhang, Jiazheng and Zhang, Kaidi and Kang, Jihua and Gui, Tao and Zhang, Qi},
  journal = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year = {2026},
  month = {Jan},
  doi = {10.18653/2026.emnlp-main.868},
  url = {https://doi.org/10.18653/2026.emnlp-main.868},
}

Chai, Mingxu et al. (2026). State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/2026.emnlp-main.868

Chai, Mingxu, et al.. "State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026, https://doi.org/10.18653/2026.emnlp-main.868.

Chai, Mingxu, et al.. "State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (2026). https://doi.org/10.18653/2026.emnlp-main.868

Chai, Mingxu et al., 2026. State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing. Available at: <https://doi.org/10.18653/2026.emnlp-main.868>.

TY  - JOUR
TI  - State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models
AU  - Chai, Mingxu
AU  - Liu, Chenyu
AU  - Shen, Ziyu
AU  - Zhang, Jiazheng
AU  - Zhang, Kaidi
AU  - Kang, Jihua
AU  - Gui, Tao
AU  - Zhang, Qi
PY  - 2026
JO  - Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
DO  - 10.18653/2026.emnlp-main.868
UR  - https://doi.org/10.18653/2026.emnlp-main.868
ER  -