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New methods enhance visual document question answering with adaptive retrieval and agentic restoration

By PulseAugur Editorial · [6 sources] ·

Researchers have developed new methods to improve Visual Document Question Answering (DocVQA) and Knowledge-Based Visual Question Answering (KB-VQA). ViSAR introduces an adaptive retrieval technique that dynamically selects the number of document pages to retrieve, reducing latency by up to 58.7% while maintaining accuracy. DocIntent focuses on agentic restoration for degraded documents, assessing question answerability and selectively applying restoration tools to improve MLLM performance. Another approach reformulates KB-VQA as a search-agent problem, allowing the agent to decide when to search, refine queries, or stop, leading to state-of-the-art results on benchmarks like InfoSeek and E-VQA. AI

IMPACT These advancements in DocVQA and KB-VQA could lead to more efficient and accurate information retrieval from visual documents, impacting fields requiring complex document analysis.

RANK_REASON The cluster contains multiple research papers detailing new methods and frameworks for visual question answering tasks.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New methods enhance visual document question answering with adaptive retrieval and agentic restoration

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The cluster contains multiple research papers detailing new methods and frameworks for visual question answering tasks.
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COVERAGE [6]

  1. arXiv cs.AI TIER_1 English(EN) · Adrien Mialland, Marc Plantevit, Julien Gallois, C\'eline Robardet ·

    ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering

    arXiv:2609.02486v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Céline Robardet ·

    ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering

    Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing appr…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Céline Robardet ·

    ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering

    Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing appr…

  4. arXiv cs.AI TIER_1 English(EN) · Zihan Huang, Shihang Wu, Junle Liu, Peirong Zhang, Yongxin Shi, Xuhan Zheng, Lianwen Jin ·

    DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering

    arXiv:2608.29037v1 Announce Type: cross Abstract: Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of Multimodal Large Language Models (MLLMs). Applying restoration tools before Visual Questi…

  5. Hugging Face Daily Papers TIER_1 English(EN) ·

    Beyond Visual Similarity: Entity-Aligned Retrieval for Knowledge-Based Visual Question Answering

    KBMR uses a multimodal language model to embed images by semantic identity rather than surface appearance, improving retrieval and visual question answering via continuous distillation and hard negative sampling.

  6. arXiv cs.CV TIER_1 English(EN) · Zhuohong Chen, Zhenxian Wu, Yunyao Yu, Hangrui Xu, Zirui Liao, Zhifang Liu, Xiangwen Deng, Pen Jiao, Haoqian Wang ·

    Learning to Search: A Decision-Based Agent for Knowledge-Based Visual Question Answering

    arXiv:2604.07146v3 Announce Type: replace Abstract: Knowledge-based visual question answering (KB-VQA) requires vision-language models to understand images and use external knowledge, especially for rare entities and long-tail facts. Most existing retrieval-augmented generation (…

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