AtomCite is an agentic framework that verifies and corrects page‑level citations in multi‑page documents by parsing answers into claims, checking each claim against the cited page image, and applying a deterministic repair policy. The authors introduce DocCite, the first benchmark for this task, built on MP‑DocVQA and DUDE, containing 928 injected instances and 1,909 verified natural errors. Across Gemini, Claude, and GPT models, AtomCite achieves about 93% verification accuracy and improves citation precision from 34% to 87‑90%, while also enhancing hallucination detection in open‑source models.
By Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos
arXiv:2608.21792v1 Announce Type: new
Abstract: Document classification in regulated industries is constrained by data residency, limited cold-start labels, scarce review capacity, and costly model-g...
By Shangxuan Tian, Yanhui Chen, Carlos Queiroz
The paper introduces an entity‑aware evaluation framework for OCR systems applied to historical documents, focusing on how well names, dates, and identifiers are preserved. Using a benchmark of 175 Berrutti Archive documents and 5,209 manually annotated entity mentions, the authors compare sequence‑recognition OCR, OCR‑specialized vision‑language models, and general‑purpose vision‑language models. Their results show that OCR rankings vary with the evaluation criterion: Qwen 3.6 achieves the highest entity coverage, while some systems recover many entities when small character‑level errors are tolerated, indicating that aggregate transcription metrics alone do not capture meaningful information preservation.
By Camilo Mari\~no (UDELAR, CB), Diego Belzarena (UDELAR, CB), Marina Gardella (CB), Ignacio Ram\'irez (UDELAR), Gregory Randall (UDELAR), Jean-Michel Morel (LU - Hong Kong)
arXiv:2603.01433v3 Announce Type: replace
Abstract: We present DOCFORGE-BENCH, the first unified zero-shot benchmark for document forgery detection, evaluating 14 methods across eight datasets spanni...
By Zengqi Zhao, Weidi Xia, En Wei, Yan Zhang, Jane Mo, Tiannan Zhang, Yuanqin Dai, Zexi Chen, Yiran Tao, Simiao Ren
arXiv:2609.14352v1 Announce Type: new
Abstract: AI-generated image detection has attracted increasing attention, but existing evaluations mainly focus on natural images, leaving AI-generated document...
By Zhangjie Fu, Jiazhen Yan, Yuanwen Chen, Xinquan Yu, Yanzhe Li, Hui Jiang, Lei Gao, Chenfu Bao
The study evaluates machine‑generated text detectors on scientific literature, finding that many detectors perform near perfectly on certain benchmarks due to superficial formatting cues rather than genuine linguistic differences. A logistic regression using 27 superficial features achieved macro‑F1 scores between 0.833 and 0.997, outperforming other detectors on arXiv subsets. The most powerful cue was the PDF extraction artifact—human texts preserve line breaks while generated texts appear as a single block—allowing classification at 0.996 AUC. By introducing format counterfactuals and normalizing formatting, the authors demonstrate that re‑wrapping machine‑written abstracts can flip 20–27% of detector decisions, reducing the generalization gap and improving out‑of‑domain performance.
By Anastasia Voznyuk, Andrey Grabovoy