Quality follows upgrading
Tag: cs.CR
arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on …
arXiv:2604.05242v1 Announce Type: new Abstract: Multi-bit watermarking has emerged as a promising solution for embedding imperceptible binary messages into Large Language Model (LLM)-generated text, enabling …
arXiv:2604.05077v1 Announce Type: new Abstract: Metal additive manufacturing (AM) enables the fabrication of safety-critical components, but reliable quality assurance depends on high-fidelity sensor streams containing …
arXiv:2604.03199v1 Announce Type: new Abstract: All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded …
arXiv:2604.02617v1 Announce Type: new Abstract: Scientific and Technical Intelligence (S&TI) analysis requires verifying complex technical claims across rapidly growing literature, where existing approaches fail to …
arXiv:2604.00788v1 Announce Type: new Abstract: This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow …
arXiv:2603.23509v1 Announce Type: new Abstract: This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): …
arXiv:2603.22350v1 Announce Type: new Abstract: Deterministic pre-execution safety gates evaluate whether individual agent actions are compatible with their assigned roles. While effective at per-action authorization, …
arXiv:2603.22525v1 Announce Type: new Abstract: Operator learning models are rapidly emerging as the predictive core of digital twins for nuclear and energy systems, promising real-time …
arXiv:2603.20208v1 Announce Type: new Abstract: Modern language models can readily extract sensitive information from unstructured text, making redaction -- the selective removal of such information …
arXiv:2603.20339v1 Announce Type: new Abstract: Many learning systems now use graph data in which each node also contains text, such as papers with abstracts or …
arXiv:2603.19258v1 Announce Type: cross Abstract: While differentially private (DP) fine-tuning of large language models (LLMs) is a powerful tool, it is often computationally prohibitive or …