arXiv:2505.07676v2 Announce Type: replace Abstract: We propose a framework for transfer learning of discount curves across different fixed-income product classes. Motivated by challenges in estimating discount curves from sparse or noisy data, we extend kernel ridge regression (KR) to a vector-valued setting, formulating a convex optimization problem in a vector-valued reproducing kernel Hilbert space (RKHS). Each component of the solution corresponds to the discount curve implied by a specific product class. We introduce an additional regularization term motivated […]

Ver mais

arXiv:2604.02457v1 Announce Type: new Abstract: Automatic license plate reader (ALPR) systems are widely deployed to identify and track vehicles. While prior work has demonstrated vulnerabilities in ALPR systems, far less attention has been paid to their legality and physical-world practicality. We investigate whether low-resourced threat actors can engineer a successful adversarial attack against a modern open-source ALPR system. We introduce the Street-legal Physical Adversarial Rim (SPAR), a physically realizable white-box attack against the popular ALPR system fast-alpr. SPAR […]

Ver mais

Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated into slide-level predictions. Heatmaps are widely used to validate MIL models and to discover tissue biomarkers. Yet, the validity of these heatmaps has barely been investigated. In this work, we introduce a general framework for evaluating the quality of MIL heatmaps without requiring additional labels. We conduct a large-scale benchmark experiment to assess six […]

Ver mais

Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank update matrix for each task. To mitigate catastrophic forgetting, state-of-the-art approaches impose constraints on new adapters with respect to the previous ones, by targeting either subspace or coordinate-wise interference. In this paper, we propose JumpLoRA, a novel framework to adaptively induce sparsity in the Low-Rank Adaptation (LoRA) blocks through the use of JumpReLU gating. The method achieves […]

Ver mais

arXiv:2602.13061v1 Announce Type: cross Abstract: The ability of Flow Matching (FM) to model complex conditional distributions has established it as the state-of-the-art for prediction tasks (e.g., robotics, weather forecasting). However, deployment in safety-critical settings is hindered by a critical extrapolation hazard: driven by smoothness biases, flow models yield plausible outputs even for off-manifold conditions, resulting in silent failures indistinguishable from valid predictions. In this work, we introduce Diverging Flows, a novel approach that enables a single model to […]

Ver mais

Cybersecurity can feel a lot like the ocean. A sense of calm on the surface, but likely something unknown is lurking underwater. Varonis Threat Labs researchers Doron Kapah and Mark Vaitsman know this reality firsthand. Most of their days involve researching how threats exfiltrate sensitive data in cloud-native environments. And knowing that AI has evolved identity management and how threats attack organizations, the duo wanted to create an Entra ID training experience that gave other security practitioners first-hand knowledge of what data exfiltration in Entra ID looks like on […]

Ver mais

British technology company Humanoid will deploy humanoid robots at factories operated by German industrial supplier Schaeffler, Reuters reported. The two companies’ agreement covers an estimated 1,000 to 2,000 robots in Schaeffler’s global manufacturing sites by 2032, according to a Humanoid spokesperson. The companies have not disclosed the contract value. The first deployment is scheduled between December 2026 and June 2027 at two Schaeffler sites in Germany. Humanoid CEO Artem Sokolov told Reuters that the initial phase will cover […]

Ver mais

Large language model (LLM) agents have recently demonstrated strong capabilities in interactive decision-making, yet they remain fundamentally limited in long-horizon tasks that require structured planning and reliable execution. Existing approaches predominantly rely on flat autoregressive policies, where high-level reasoning and low-level actions are generated within a single token sequence, leading to inefficient exploration and severe error propagation over extended trajectories. In this work, we propose HiMAC, a hierarchical agentic RL framework that explicitly decomposes long-horizon decision-making into macro-level […]

Ver mais

arXiv:2602.09162v1 Announce Type: new Abstract: Analog Ising machines (AIMs) have emerged as a promising paradigm for combinatorial optimization, utilizing physical dynamics to solve Ising problems with high energy efficiency. However, the performance of traditional optimization and sampling algorithms on these platforms is often limited by inherent measurement noise. We introduce BRAIN (Boltzmann Reinforcement for Analog Ising Networks), a distribution learning framework that utilizes variational reinforcement learning to approximate the Boltzmann distribution. By shifting from state-by-state sampling to aggregating […]

Ver mais