arXiv AI & Jobs Research (Fahad Shamshad, Nils Lukas, Karthik Nandakumar) • Published: 2026-01-13
Invisible watermarking has become a critical mechanism for authenticating AI-generated image content, with major platforms deploying watermarking schemes at scale. However, evaluating the vulnerability of these schemes against sophisticated removal attacks remains essential to assess their reliability and guide robust design. In this work, we expos
arXiv AI & Jobs Research (Yang Cai, Weiqiang Zheng) • Published: 2026-01-13
Aligning large language models (LLMs) to serve users with heterogeneous and potentially conflicting preferences is a central challenge for personalized and trustworthy AI. We formalize an ideal notion of universal alignment through test-time scaling: for each prompt, the model produces $k\ge 1$ candidate responses and a user selects their preferred
arXiv AI & Jobs Research (A. M. A. S. D. Alagiyawanna, Asoka Karunananda, Th) • Published: 2026-01-13
Artificial Intelligence (AI) systems have shown good success at classifying. However, the lack of explainability is a true and significant challenge, especially in high-stakes domains, such as health and finance, where understanding is paramount. We propose a new solution to this challenge: an explainable AI framework based on our comparative study
arXiv AI & Jobs Research (Vincent Roca, Martin Bretzner, Hilde Henon, Lauren) • Published: 2026-01-13
Accurate delineation of acute ischemic stroke lesions in MRI is a key component of stroke diagnosis and management. In recent years, deep learning models have been successfully applied to the automatic segmentation of such lesions. While most proposed architectures are based on the U-Net framework, they primarily differ in their choice of loss func
arXiv AI & Jobs Research (Nifu Dan) • Published: 2026-01-13
As generative AI becomes embedded in higher education, it increasingly shapes how students complete academic tasks. While these systems offer efficiency and support, concerns persist regarding over-automation, diminished student agency, and the potential for unreliable or hallucinated outputs. This study conducts a mixed-methods audit of student-AI
arXiv AI & Jobs Research (Marco Tonnarelli, Filippo Scaramuzza, Simon Harrer) • Published: 2026-01-13
Computational data governance aims to make the enforcement of governance policies and legal obligations more efficient and reliable. Recent advances in natural language processing and agentic AI offer ways to improve how organizations share and use data. But many barriers remain. Today's tools require technical skills and multiple roles to discover
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