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The paper introduces Answerable Working Memory (AWM) and AWM-GRPO to enhance the quality of terminal working memory in long-document VQA agents, improving accuracy and reducing memory issues.
The paper proposes Trident, a method that enhances long-document visual question answering through structured multi-aspect page annotation for reranking and synthesis, improving evidence selection and answer generation accuracy.
Introduces XL-DocBench, a human-verified benchmark for extra-long document understanding with 1,519 questions across six professional domains, requiring multi-page evidence and structured reasoning, showing current LLMs still struggle with long-context professional documents.
Introduces MultAttnAttrib, a training-free method for multimodal attribution in long document QA, along with the MultAttrEval benchmark. It outperforms prompting-based methods and matches frontier models like GPT-5.4.
Baidu open-sources Unlimited OCR, a 3B parameter model (500M activated) that reads long documents in a single pass using Reference Sliding Window Attention (R-SWA), achieving state-of-the-art results on OmniDocBench.
This paper proposes an attention expansion mechanism to enhance keyphrase extraction from long documents by augmenting PLM token representations with out-of-context information, achieving consistent improvements over state-of-the-art models without requiring full-document attention or expensive LLM inference.
The article provides an overview of 20 advanced RAG (Retrieval-Augmented Generation) types expected to be relevant in 2026, covering long-document memory, adaptive retrieval, multimodal grounding, multilingual QA, graph reasoning, and security-focused RAG approaches.
A benchmark comparing vision-capable LLMs (native PDF reading) against OCR-based pipelines on 30 long, image-heavy PDFs finds that OCR with layout extraction still outperforms vision models on chart/table-heavy pages and has a 0% failure rate vs. 7% for native PDF, though the sample size is small and many gaps are within noise.
This paper presents a real-time verification system for retrieval-augmented generation that processes long documents up to 32K tokens, using adaptive inference strategies to balance latency and verification coverage. It provides practical guidance for building reliable RAG systems.