arXiv:2511.21600v1 Announce Type: cross Abstract: The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns about data provenance and misuse. Watermarking offers a promising…
arXiv:2511.20798v1 Announce Type: new Abstract: Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but also distinct, human-understandable abstract concepts and behaviour. Moreover, these hidden features can be…
arXiv:2406.19622v2 Announce Type: replace Abstract: As deep neural networks (DNNs) are increasingly deployed in sensitive applications, ensuring their security and robustness has become critical. A major threat to DNNs arises from adversarial attacks, where small input perturbations can lead to…
arXiv:2511.20811v1 Announce Type: new Abstract: We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations can arise unexpectedly as a result of these uncertainties, pilots need…
arXiv:2505.18636v2 Announce Type: replace Abstract: The go-to strategy to apply deep networks in settings where uncertainty informs decisions–ensembling multiple training runs with random initializations–is ill-suited for the extremely large-scale models and practical fine-tuning workflows of today. We introduce a new…
arXiv:2511.20826v1 Announce Type: new Abstract: Untrained large neural networks, just after random initialization, tend to favour a small subset of classes, assigning high predicted probabilities to these few classes and approximately zero probability to all others. This bias, termed Initial…
arXiv:2511.20830v1 Announce Type: new Abstract: The solar wind, a continuous outflow of charged particles from the Sun’s corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is…
arXiv:2511.10234v2 Announce Type: replace Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operating on sequential graph serializations, LLMs can produce different outputs under node reindexing, edge reordering, or formatting…
arXiv:2511.20839v1 Announce Type: new Abstract: We present Primal, a deterministic feature mapping framework that harnesses the number-theoretic independence of prime square roots to construct robust, tunable vector representations. Diverging from standard stochastic projections (e.g., Random Fourier Features), our method exploits…
arXiv:2511.20099v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural language descriptions that are often ambiguous, redundant, and unstructured, which poses significant challenges…
