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HiPS: Hierarchical PDF Segmentation of Doctrinal Legal Books

Announce Type: replace Abstract: PDF parsers have recently improved on page-level layout understanding. However, recovering a document-global section hierarchy with reliable boundaries remains brittle for deeply structured books: many systems expose only page-local heading roles, assume shallow depth, or rely on high-quality PDF tags or Table of Contents (TOC) metadata, and public gold-standard data for deep book hierarchies is scarce. We present HiPS for hierarchical PDF segmentation of...

arXiv CS 2d ago

Beyond String Matching: Semantic Evaluation of PDF Table Extraction

arXiv:2603.18652v2 Announce Type: replace Abstract: Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content. We present a benchmarking framework based on synthetically generated PDFs with precise LaTeX ground truth, using tables sourced from arXiv to ensure realistic complexity and diversity. As our central...

arXiv CS 8d ago

System Card: Claude Fable 5 and Claude Mythos 5 [pdf]

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Hacker News 23h ago

Do Women's Mate Preferences Change Across the Ovulatory Cycle? A Meta-Analysis [pdf]

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Surveillance Is Not Safety: A statement on the UK's latest threat to privacy [pdf]

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Let's Encrypt bans certificate usage in any US sanctioned territory [pdf]

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A Mathematician's Lament – Paul Lockhart (2002) [pdf]

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Hacker News 6d ago

Frost: [Browser] Fingerprinting Remotely Using OPFS-Based SSD Timing [pdf]

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Hacker News 10d ago

Efficient Weighted Sampling via Score-based Generative Models

Announce Type: replace Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more. Leveraging the increasing availability of pretrained score-based generative models (SGMs), we propose a training-free weighted sampling framework that approximates the backward diffusion process of...

arXiv CS 8d ago