arXiv Computer Vision
VisDocAgentBench: Benchmarking Agents for Visually Rich Document Retrieval
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VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents
arXiv:2607. 24748v1 Announce Type: cross Abstract: 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, charts, and figures.
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TechRAG: Evidence-Gated Multimodal Agentic RAG for Technical Literature Reasoning
arXiv:2606. 01613v2 Announce Type: replace-cross Abstract: This paper presents an agentic multimodal retrieval-augmented generation (RAG) framework for domain-specific literature reasoning, instantiated on a curated corpus of several thousand papers in intelligent tires, vehicle dynamics, vehicle control, sensing, estimation, and machine learning.
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VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning
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Hugging Face Trending Papers
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VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval
Visual document retrieval has recently become increasingly important in applications such as enterprise search, scientific literature discovery, and retrieval-augmented generation. These applications depend on efficiently identifying query-relevant pages across large collections of visually rich documents.
arXiv AI
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Reason Before You Retrieve: Agentic Planning for Multi-modal RAG
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arXiv Computation and Language
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Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA
arXiv:2604. 13731v2 Announce Type: replace Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents.