arXiv:2603.28198v1 Announce Type: new
Abstract: Static regret to a single expert is often the wrong target for strictly online prediction under non-stationarity, where the best expert may switch repeatedly over time. We study Policy-Controlled General…
arXiv:2602.08927v2 Announce Type: replace-cross
Abstract: We study the problem of online monotone density estimation, where density estimators must be constructed in a predictable manner from sequentially observed data. We propose two online estimator…
arXiv:2603.28173v1 Announce Type: new
Abstract: Data-driven weather models have advanced global medium-range forecasting, yet high-resolution regional prediction remains challenging due to unresolved multiscale interactions between large-scale dynamic…
arXiv:2601.16771v2 Announce Type: replace-cross
Abstract: Previous representation and generation approaches for the B-rep relied on graph-based representations that disentangle geometric and topological features through decoupled computational pipelin…
arXiv:2603.28167v1 Announce Type: new
Abstract: This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructured discharge reports. The proposed pipeline uses disch…
arXiv:2601.01095v2 Announce Type: replace-cross
Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in videos remains undere…
arXiv:2603.28128v1 Announce Type: new
Abstract: Although Graph Neural Networks (GNNs) have shown promise for smart contract vulnerability detection, they still face significant limitations. Homogeneous graph models fail to capture the interplay betwee…
arXiv:2512.02711v2 Announce Type: replace-cross
Abstract: Ensuring content safety in large language models (LLMs) is essential for their deployment in real-world applications. However, existing safety guardrails are predominantly tailored for high-res…
arXiv:2603.28117v1 Announce Type: new
Abstract: Livestock growth prediction is essential for optimising farm management and improving the efficiency and sustainability of livestock production, yet it remains underexplored due to limited large-scale da…
arXiv:2511.18151v3 Announce Type: replace-cross
Abstract: Unmanned Aerial Vehicles (UAVs) in disaster response require complex, queryable intelligence that onboard CNNs cannot provide. While Vision-Language Models (VLMs) offer this semantic reasoning,…