arXiv:2601.22709v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significant accuracy loss. Despite its potential, quantization-aware training for VLMs remains underexplored. We propose GRACE, a framework unifying knowledge distillation and QAT under the Information Bottleneck principle: quantization constrains information capacity while distillation […]
Page image classification for content-specific data processing
arXiv:2507.21114v3 Announce Type: replace-cross Abstract: Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text types (handwritten, typed, printed), graphical elements (drawings, maps, photos), and layouts (plain text, tables, forms). Efficiently processing this heterogeneous data […]
Adaptive Differential Privacy for Federated Medical Image Segmentation Across Diverse Modalities
arXiv:2604.06518v2 Announce Type: replace-cross Abstract: Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and institutional constraints. In addition, models trained in centralized settings frequently fail to generalize across clinical sites because of heterogeneity in imaging protocols and continuously evolving data distributions arising from differences […]
Cross-Polarization Fusion of VV AND VH SAR Observations for Improved Flood Mapping
arXiv:2605.02153v1 Announce Type: cross Abstract: Synthetic Aperture Radar (SAR) imagery is widely used for flood monitoring due to its all-weather and day-night imaging capability. However, flood mapping using single-polarization SAR data remains challenging in complex environments where surface and volume scattering coexist. In this paper, we investigate the effectiveness of cross-polarization fusion of VV and […]
From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model
arXiv:2605.02491v1 Announce Type: cross Abstract: Modern searches for physics beyond the Standard Model produce rapidly expanding literature containing heterogeneous information, including textual analyses, numerical datasets, and graphical exclusion limits. Integrating these distributed sources remains a time-consuming and manual process for physicists. We present HEP-CoPilot, a retrieval-augmented multi-agent AI framework for the exploration and interpretation of […]
Static Analysis of Recursive SHACL
arXiv:2605.02787v1 Announce Type: cross Abstract: SHACL (Shapes Constraint Language) expresses constraints on RDF data by means of so-called shapes. Its central service is validation: verifying whether a data graph complies with a SHACL document. But so far, there are no static analysis services to compare documents. In this paper, we study the following problem: decide […]
Can Semantic Methods Enhance Team Sports Tactics? A Methodology for Football with Broader Applications
arXiv:2601.00421v2 Announce Type: replace Abstract: This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in team sports. Building on the analogy between texts and teams — where players act as words and collective play conveys meaning — the proposed methodology models tactical configurations as compositional semantic structures. […]
GDPR Auto-Formalization with AI Agents and Human Verification
arXiv:2604.14607v2 Announce Type: replace Abstract: We study the overall process of automatic formalization of GDPR provisions using large language models, within a human-in-the-loop verification framework. Rather than aiming for full autonomy, we adopt a role-specialized workflow in which LLM-based AI components, operating in a multi-agent setting with iterative feedback, generate legal scenarios, formal rules, and […]
StereoMamba: Real-time and Robust Intraoperative Stereo Disparity Estimation via Long-range Spatial Dependencies
arXiv:2504.17401v2 Announce Type: replace-cross Abstract: Stereo disparity estimation is crucial for obtaining depth information in robot-assisted minimally invasive surgery (RAMIS). While current deep learning methods have made significant advancements, challenges remain in achieving an optimal balance between accuracy, robustness, and inference speed. To address these challenges, we propose the StereoMamba architecture, which is specifically designed […]
The Coding Limits of Robust Watermarking for Generative Models
arXiv:2509.10577v3 Announce Type: replace-cross Abstract: We study a basic question about cryptographic watermarking for generative models: how reliable can a watermark remain when an adversary is allowed to corrupt the encoded signal? To address this question, we introduce a minimal coding abstraction that we call a zero-bit tamper-detection code. This is a secret-key procedure that […]
FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control
arXiv:2603.12612v2 Announce Type: replace-cross Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ”curse of dimensionality” induces severe exploration inefficiency and training instability. Consequently, highly optimized deterministic policy gradients currently dominate high-throughput regimes. We address this limitation with FastDSAC, a framework that effectively unlocks the potential of […]
Short-wave signal versus indirect prey-taxis
arXiv:2604.20469v2 Announce Type: replace-cross Abstract: We address a short-wave asymptotic for one class of quasi-linear second-order PDE systems involving the cross-diffusion described by the so-called Patlak-Keller-Segel law. It is common to employ these equations for modeling the predator-prey community with the prey-taxis that means the interactions of two species of particles or cells or anything […]