A text-supervised global layout embedding augments local patch representations in late-interaction VDR, yielding +2.4 nDCG@5 and +2.3 MAP@5 gains over ColPali/ColQwen baselines on ViDoRe-v2.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
OptiVerse is a new benchmark spanning neglected optimization domains that shows LLMs suffer sharp accuracy drops on hard problems due to modeling and logic errors, with a Dual-View Auditor Agent proposed to improve performance.
PA-BDM adapts block diffusion by switching to causal intra-block denoising and dynamically committing reliable prefixes to KV cache, yielding higher accuracy and 71.6% higher throughput than a comparable baseline on document benchmarks.
High OCR accuracy on standard metrics does not guarantee strong downstream RAG performance because structural and semantic errors cause retrieval and generation failures on challenging industrial documents.
citing papers explorer
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Beyond Bag-of-Patches: Learning Global Layout via Textual Supervision for Late-Interaction Visual Document Retrieval
A text-supervised global layout embedding augments local patch representations in late-interaction VDR, yielding +2.4 nDCG@5 and +2.3 MAP@5 gains over ColPali/ColQwen baselines on ViDoRe-v2.
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OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
OptiVerse is a new benchmark spanning neglected optimization domains that shows LLMs suffer sharp accuracy drops on hard problems due to modeling and logic errors, with a Dual-View Auditor Agent proposed to improve performance.
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Prefix-Adaptive Block Diffusion for Efficient Document Recognition
PA-BDM adapts block diffusion by switching to causal intra-block denoising and dynamically committing reliable prefixes to KV cache, yielding higher accuracy and 71.6% higher throughput than a comparable baseline on document benchmarks.
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When Good OCR Is Not Enough: Benchmarking OCR Robustness for Retrieval-Augmented Generation
High OCR accuracy on standard metrics does not guarantee strong downstream RAG performance because structural and semantic errors cause retrieval and generation failures on challenging industrial documents.