PureDocBench shows document parsing is far from solved, with top models at ~74/100, small specialists competing with large VLMs, and ranking reversals under real degradation.
Dolphin-v2: Universal document parsing via scalable anchor prompting
10 Pith papers cite this work. Polarity classification is still indexing.
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ASAHI adaptively slices high-res images into 6 or 12 patches, adds slicing-assisted fine-tuning, and uses Cluster-DIoU-NMS to hit 56.8% mAP on VisDrone2019 and 22.7% on xView while running 20-25% faster than fixed slicing baselines.
P-MTP uses progressive curriculum loss and confidence-gated dynamic drafting to scale look-ahead depth in multi-token prediction, claiming up to 5x speedup with negligible accuracy loss in document parsing.
MPDocBench-Parse provides 433 annotated multi-page documents and an evaluation protocol covering text/table/formula extraction, merging, figure extraction, reading order, and heading hierarchy for realistic document parsing.
An ablation study of an RL-based jailbreaker finds the attack succeeds across tested open-weight models and safeguards, but its headline conclusion about dense rewards and long episodes is contradicted by its own data.
ABot-OCR is a new end-to-end VLM for direct image-to-Markdown transcription using a custom data engine and structure-constrained RL optimization, reporting SOTA scores of 92.81/93.30 on OmniDocBench v1.5/v1.6.
LUT-Opt distills XGBoost regressors into lookup tables to enable sub-millisecond adaptive optimization of rendering parameters such as subsurface scattering and ambient occlusion.
FPFNet reports state-of-the-art AUROC scores on MVTec-AD and VisA for unified multi-class defect detection by adding feature perturbation and hierarchical fusion to UniAD with no extra parameters.
A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
RDCNet reports state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof by combining random dilated convolutions with multi-branch and attention modules.
citing papers explorer
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How Far Is Document Parsing from Solved? PureDocBench: A Source-TraceableBenchmark across Clean, Degraded, and Real-World Settings
PureDocBench shows document parsing is far from solved, with top models at ~74/100, small specialists competing with large VLMs, and ranking reversals under real degradation.
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Adaptive Slicing-Assisted Hyper Inference for Enhanced Small Object Detection in High-Resolution Imagery
ASAHI adaptively slices high-res images into 6 or 12 patches, adds slicing-assisted fine-tuning, and uses Cluster-DIoU-NMS to hit 56.8% mAP on VisDrone2019 and 22.7% on xView while running 20-25% faster than fixed slicing baselines.
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P-MTP: Efficient Document Parsing via Multi-Token Prediction with Progressive Depth Scaling
P-MTP uses progressive curriculum loss and confidence-gated dynamic drafting to scale look-ahead depth in multi-token prediction, claiming up to 5x speedup with negligible accuracy loss in document parsing.
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MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing
MPDocBench-Parse provides 433 annotated multi-page documents and an evaluation protocol covering text/table/formula extraction, merging, figure extraction, reading order, and heading hierarchy for realistic document parsing.
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A Systematic Investigation of RL-Jailbreaking in LLMs
An ablation study of an RL-based jailbreaker finds the attack succeeds across tested open-weight models and safeguards, but its headline conclusion about dense rewards and long episodes is contradicted by its own data.
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ABot-OCR Technical Report
ABot-OCR is a new end-to-end VLM for direct image-to-Markdown transcription using a custom data engine and structure-constrained RL optimization, reporting SOTA scores of 92.81/93.30 on OmniDocBench v1.5/v1.6.
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Lightweight Real-Time Rendering Parameter Optimization via XGBoost-Driven Lookup Tables
LUT-Opt distills XGBoost regressors into lookup tables to enable sub-millisecond adaptive optimization of rendering parameters such as subsurface scattering and ambient occlusion.
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Feature Perturbation Pool-based Fusion Network for Unified Multi-Class Industrial Defect Detection
FPFNet reports state-of-the-art AUROC scores on MVTec-AD and VisA for unified multi-class defect detection by adding feature perturbation and hierarchical fusion to UniAD with no extra parameters.
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Hierarchical Awareness Adapters with Hybrid Pyramid Feature Fusion for Dense Depth Prediction
A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
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Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation
RDCNet reports state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof by combining random dilated convolutions with multi-branch and attention modules.