arXiv:2602.18483v1 Announce Type: new Abstract: Recently, red teaming, with roots in security, has become a key evaluative approach to ensure the safety and reliability of Generative Artificial Intelligence. However, most existing work emphasizes technical benchmarks and attack success rates, leaving the socio-technical practices of how red teaming datasets are defined, created, and evaluated under-examined. Drawing on 22 interviews with practitioners who design and evaluate red teaming datasets, we examine the data practices and standards that underpin this work. […]

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(A senior engineer perspective on bucket metadata, negative caches, and surviving random-key floods) Introduction At internet scale, object storage is less about moving bytes and more about answering questions quickly: does this bucket exist, is this caller allowed, where is this object, and what exactly should we return? Those are metadata questions. This is why metadata becomes the real bottleneck. Every request touches it, including failures. A 404 still requires the system to prove absence. A HEAD is […]

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Effective models of Cyber-Physical Systems (CPS) are crucial for their design and operation. Constructing such models is difficult and time-consuming due to the inherent complexity of CPS. As a result, data-driven model generation using machine learning methods is gaining popularity. In this paper, we present Flowcean, a novel framework designed to automate the generation of models through data-driven learning that focuses on modularity and usability. By offering various learning strategies, data processing methods, and evaluation metrics, our framework […]

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arXiv:2601.12194v1 Announce Type: new Abstract: We develop an information-theoretic framework for discrete dynamics grounded in a comparison-cost functional on ratios. Given two quantities compared via their ratio (x=a/b), we assign a cost (F(x)) measuring deviation from equilibrium ((x=1)). Requiring coherent composition under multiplicative chaining imposes a d’Alembert functional equation; together with normalization ((F(1)=0)) and quadratic calibration at unity, this yields a unique reciprocal cost functional (proved in a companion paper): [ J(x) = tfrac{1}{2}bigl(x + x^{-1}bigr) – 1. […]

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Open-set active learning (OSAL) aims to identify informative samples for annotation when unlabeled data may contain previously unseen classes-a common challenge in safety-critical and open-world scenarios. Existing approaches typically rely on separately trained open-set detectors, introducing substantial training overhead and overlooking the supervisory value of labeled unknowns for improving known-class learning. In this paper, we propose E$^2$OAL (Effective and Efficient Open-set Active Learning), a unified and detector-free framework that fully exploits labeled unknowns for both stronger supervision and […]

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arXiv:2602.15904v1 Announce Type: new Abstract: LiDAR sensors are often considered essential for autonomous driving, but high-resolution sensors remain expensive while affordable low-resolution sensors produce sparse point clouds that miss critical details. LiDAR super-resolution addresses this challenge by using deep learning to enhance sparse point clouds, bridging the gap between different sensor types and enabling cross-sensor compatibility in real-world deployments. This paper presents the first comprehensive survey of LiDAR super-resolution methods for autonomous driving. Despite the importance of practical […]

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arXiv:2601.00110v1 Announce Type: new Abstract: In this paper, we present textit{CTMAP}, a large language model (LLM) empowered digital twin framework for connectivity-aware route navigation in millimeter-wave (mmWave) wireless networks. Conventional navigation tools optimize only distance, time, or cost, overlooking network connectivity degradation caused by signal blockage in dense urban environments. The proposed framework constructs a digital twin of the physical mmWave network using OpenStreetMap, Blender, and NVIDIA Sionna’s ray-tracing engine to simulate realistic received signal strength (RSS) maps. […]

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arXiv:2601.09037v1 Announce Type: new Abstract: Probabilistic computers built from p-bits offer a promising path for combinatorial optimization, but the dense connectivity required by real-world problems scales poorly in hardware. Here, we address this through graph sparsification with auxiliary copy variables and demonstrate a fully on-chip parallel tempering solver on an FPGA. Targeting MIMO detection, a dense, NP-hard problem central to wireless communications, we fit 15 temperature replicas of a 128-node sparsified system (1,920 p-bits) entirely on-chip and achieve […]

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In a world where ransomware strikes, attempts at data manipulation, and system-crippling intrusions against hospitals are on the rise, the healthcare sector has reached a breaking point. These are no longer attacks on mere data; they are on ventilators, on imaging machines, on diagnostic algorithms, and on the very systems that sustain patients’ lives. On a national level across the United States, even a single breach has the power to halt surgeries, divert ambulances, and put lives in […]

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Model-based reinforcement learning (MBRL) improves sample efficiency by leveraging learned dynamics models for policy optimization. However, the effectiveness of methods such as actor-critic is often limited by compounding model errors, which degrade long-horizon value estimation. Existing approaches, such as Model-Based Value Expansion (MVE), partially mitigate this issue through multi-step rollouts, but remain sensitive to rollout horizon selection and residual model bias. Motivated by the Pontryagin Maximum Principle (PMP), we propose Hamiltonian Actor-Critic (HAC), a model-based approach that eliminates […]

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