arXiv:2603.23146v1 Announce Type: cross Abstract: The widespread adoption of Large Language Models (LLMs) has made the detection of AI-Generated text a pressing and complex challenge. Although many detection systems report high benchmark accuracy, their reliability in real-world settings remains uncertain, and their interpretability is often unexplored. In this work, we investigate whether contemporary detectors genuinely identify machine authorship or merely exploit dataset-specific artefacts. We propose an interpretable detection framework that integrates linguistic feature engineering, machine learning, and explainable AI techniques. When evaluated on two prominent benchmark corpora, namely PAN CLEF 2025 and COLING 2025, our model trained on 30 linguistic features achieves leaderboard-competitive performance, attaining an F1 score of 0.9734. However, systematic cross-domain and cross-generator evaluation reveals substantial generalisation failure: classifiers that excel in-domain degrade significantly under distribution shift. Using SHAP- based explanations, we show that the most influential features differ markedly between datasets, indicating that detectors often rely on dataset-specific stylistic cues rather than stable signals of machine authorship. Further investigation with in-depth error analysis exposes a fundamental tension in linguistic-feature-based AI text detection: the features that are most discriminative on in-domain data are also the features most susceptible to domain shift, formatting variation, and text-length effects. We believe that this knowledge helps build AI detectors that are robust across different settings. To support replication and practical use, we release an open-source Python package that returns both predictions and instance-level explanations for individual texts.

arXiv:2503.22769v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) has been advancing rapidly and with the advent of large language models (LLMs) in late 2022, numerous opportunities have emerged for adopting this technology across various domains, including medicine. These innovations hold immense potential to revolutionize and modernize medical education. Our research project leverages large language models to enhance medical education and address workflow challenges through the development of MediTools - AI Medical Education. This prototype application focuses on developing interactive tools that simulate real-life clinical scenarios, provide access to medical literature, and keep users updated with the latest medical news. Our first tool is a dermatology case simulation tool that uses real patient images depicting various dermatological conditions and enables interaction with LLMs acting as virtual patients. This platform allows users to practice their diagnostic skills and enhance their clinical decision-making abilities. The application also features two additional tools: an AI-enhanced PubMed tool for engaging with LLMs to gain deeper insights into research papers, and a Google News tool that offers LLM generated summaries of articles for various medical specialties. A comprehensive survey has been conducted among medical professionals and students to gather initial feedback on the effectiveness and user satisfaction of MediTools, providing insights for further development and refinement of the application. This research demonstrates the potential of AI-driven tools in transforming and revolutionizing medical education, offering a scalable and interactive platform for continuous learning and skill development.

arXiv:2602.19066v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow inference, limiting practical use. To address this, we extend Inverse Distillation, a technique originally developed to accelerate continuous diffusion models, to the discrete setting. Nonetheless, this extension introduces both theoretical and practical challenges. From a theoretical perspective, the inverse distillation objective lacks uniqueness guarantees, which may lead to suboptimal solutions. From a practical standpoint, backpropagation in the discrete space is non-trivial and often unstable. To overcome these challenges, we first provide a theoretical result demonstrating that our inverse formulation admits a unique solution, thereby ensuring valid optimization. We then introduce gradient-stable relaxations to support effective training. As a result, experiments on multiple DLMs show that our method, Inverse-distilled Diffusion Language Models (IDLM), reduces the number of inference steps by 4x-64x, while preserving the teacher model's entropy and generative perplexity.