OCR-EDR is a rendering‑aware framework that diagnoses and repairs OCR errors by comparing an editable OCR prediction with its rendered image. It jointly assesses consistency, localizes genuine errors, and applies executable edits, optionally requesting updated renderings for iterative reassessment. On the newly constructed OCRErrBench, the DocEDR model achieves 94.78% diagnostic accuracy and repairs 86.23% of erroneous inputs, improving formula metrics by up to 30.99 percentage points on benchmark datasets.
By Linnan Zhao, Kang Liu, Hao Yu, Jiabo Zhan, Chong Sun, Chen Li
OCR-EDR is a rendering‑aware framework that diagnoses and repairs OCR errors by jointly evaluating an OCR prediction, its editable form, and the rendered image of the source document. It localizes genuine mistakes while preserving valid or rendering‑equivalent predictions, then applies executable edits and iteratively reassesses with updated renderings. On the newly constructed OCRErrBench, the DocEDR model achieves 94.78% diagnostic accuracy and repairs 86.23% of errors, boosting formula metrics by up to 30.99 percentage points and improving CDM scores on several OCR systems.
arXiv:2609.14521v1 Announce Type: new
Abstract: Recovering editable 3D parametric curves from 2D images is a fundamental challenge in computer graphics, bridging pixel-based perception and vector-bas...
By Zhirui Gao, Renjiao Yi, Yunfan Ye, Ruizhen Hu, Chenyang Zhu, Wei Chen, Kai Xu
arXiv:2606. 31764v2 Announce Type: replace-cross Abstract: Differentiable rendering of planar rational splines remains largely underexplored, despite their widespread use in vector graphics and design.
By Jingye Qiu, Shizhe Zhou
PlotPick is an open‑source tool that employs vision‑language models (VLMs) to batch‑extract tabular data from scientific figures. In benchmarks on synthetic chart datasets (ChartX and PlotQA), nine VLMs generally outperform the dedicated chart‑to‑table model DePlot, especially on box plots and horizontal bar charts. The tool’s figure detection, output formatting, and default model (Claude Haiku 4.5) were not evaluated, and accuracy on real biomedical figures remains unverified.
By Tommy Carstensen
The paper introduces Sharp Mode Connectivity (SMC), a new formulation of mode connectivity that optimizes for flatness in the entire local neighborhood of a weight-space path rather than just along the path itself. By applying a sharpness-aware approximation to a minimax objective, SMC produces low-loss curves that remain robust to distribution shifts, achieving significant accuracy gains on CIFAR‑10‑C and outperforming standard mode connectivity on several architectures.
By Alejandro Calatrava, Xu Zhang, Ren Wang
VisAudit is a new benchmark that tests multimodal agents on visual diagnosis, repair, and verification tasks. It presents agents with rendered charts and auxiliary evidence—such as source data, intended summaries, and code—to iteratively detect defects, modify the visualization, and confirm successful repairs. The benchmark includes 1,900 flawed charts across 21 types and 10 flaw categories, plus 300 correct charts, and shows that current models recover only about 47.4% of flawed charts autonomously.
By Shicheng Liu, Adam Kahirov, Qi Zhang, Zhimin Hu, Song Wang, Junhong Lin, Julian Shun, Yada Zhu
FigMirror is a new framework that converts scientific figures into executable code, enabling the creation of new plots that preserve the visual style of a reference figure. It achieves this by using Grounded Measurement to locate visual elements via coordinates and measure their properties through code, overcoming limitations of pixel‑level optimization used by prior methods. The authors also introduce PlotTwin‑Bench, a benchmark with fine‑grained code and image‑level style metrics, and demonstrate that FigMirror consistently outperforms existing style‑transfer approaches.
By Xiaohan Zhao, Jiacheng Liu, Yaxin Luo, Zhiqiang Shen
arXiv:2609.36779v1 Announce Type: cross
Abstract: Accurate hand-eye calibration is crucial for precision manipulation. Traditional methods rely on markers, with their precision dependent on marker ac...
By Xiaotian Zhang, Yusheng Wang, Naoya Kagawa, Noritaka Takamura, Keiji Okuhara, Hiroyasu Baba, Jun Ota
ChartDensity-Bench is a benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to reconstruct structured numerical data from scientific charts that vary in visual density. The benchmark uses charts paired with source-level ground-truth data and systematically changes the number of simultaneously presented charts (k = 1, 3, 6, 9) to assess how density affects reconstruction performance. A multi‑dimensional evaluation framework measures structural reliability, reconstruction completeness, parseability, and numerical fidelity, revealing that numerical reconstruction generally worsens as visual density increases, with varying degrees of degradation across different models.
By Xinhe Wu, Yadong Jin
arXiv:2608.30161v1 Announce Type: new
Abstract: Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping...
By Therin Young, Elijah Rodriguez, Lisa Coffey, Talukder Zaki Jubery, Adarsh Krishnamurthy, Patrick Schnable, Baskar Ganapathysubramanian
MatReplace is a new reference‑free benchmark for evaluating material replacement in interior scenes, assessing edits on local material correctness, global lighting harmony, outside preservation, and inside structure. It offers three tracks that vary the conditioning signal—instruction only, instruction plus region mask, and material reference image—allowing systematic comparison of different editing approaches. Results show that while closed‑source editors excel at named‑material rendering, grounding materials from pixel references remains a significant challenge.
By Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy, See-Kiong Ng, Luu Anh Tuan