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cs.IR2026
VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents
Seonok Kim
Visually-rich documents such as reports, slides, and manuals often distribute the evidence needed to answer a question across multiple pages, mixing text with layout cues, tables,…
cs.IR2026
LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrieval
Seonok Kim
Retrieving relevant evidence from visually rich documents such as textbooks, technical reports, and manuals is challenging due to long context, complex layouts, and weak lexical ov…