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cs.LG2026
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
cs.CL2026
SciMDR: Advancing Scientific Multimodal Document Reasoning
Ziyu Chen, Yilun Zhao, Chengye Wang +3
Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and realism. To address this…
cs.CL2026
TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction
Chengye Wang, Lin Fu, Zexi Kuang +1
Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientific publishing. We study page…