Diffusion and flow models achieve State-Of-The-Art (SOTA) generative performance, yet many practically important behaviors such as fine-grained prompt fidelity, compositional correctness, and text rendering are weakly specified by score or flow matching pretraining objectives. Reinforcement Learning (RL) fine-tuning with external, black-box rewards is a natural remedy, but diffusion RL is often brittle. Trajectory-based methods incur high memory cost and high-variance gradient estimates; forward-process approaches converge faster but can suffer from distribution drift, and hence reward hacking. In this […]

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arXiv:2602.04971v1 Announce Type: new Abstract: In this work, we study multi-sensor scheduling for remote state estimation over wireless multiple-input multiple-output (MIMO) fading channels using a novel semantic over-the-air (SemOTA) aggregation approach. We first revisit Kalman filtering with conventional over-the-air (OTA) aggregation and highlight its transmit power limitations. To balance power efficiency and estimation performance, we formulate the scheduling task as a finite-horizon dynamic programming (DP) problem. By analyzing the structure of the optimal Q-function, we show that the […]

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Legal Intern Alexandra Rhodes contributed to this blog post.  EFF filed an amicus brief urging the Arizona District Court to protect public school students’ freedom of speech and privacy by holding that the use of a school-issued laptop or email account does not categorically mean a student is “on campus.” We argued that students need private digital spaces beyond their school’s reach to speak freely, without the specter of constant school surveillance and punishment.   Surveillance Software Exposed a […]

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arXiv:2602.23556v1 Announce Type: new Abstract: Large-scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex’s neighbors to a fixed distance. Because large input graphs are distributed, training requires frequent irregular communication that stalls forward progress. Moreover, fetched data changes with graph, graph distribution, sample and batch parameters, and caching polices. Consequently, any static prefetching method will miss crucial opportunities to adapt to different dynamic conditions. In this paper, we introduce Rudder, a software module embedded in […]

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arXiv:2602.05799v1 Announce Type: cross Abstract: We study non-stationary single-item, periodic-review inventory control problems in which the demand distribution is unknown and may change over time. We analyze how demand non-stationarity affects learning performance across inventory models, including systems with demand backlogging or lost-sales, both with and without lead times. For each setting, we propose an adaptive online algorithm that optimizes over the class of base-stock policies and establish performance guarantees in terms of dynamic regret relative to the […]

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arXiv:2604.16957v1 Announce Type: new Abstract: We present Open-TQ-Metal, the first implementation of fused compressed-domain attention on Apple Silicon, enabling 128K-context inference for Llama 3.1 70B on a single 64GB consumer Mac — a configuration impossible with all existing inference frameworks. Open-TQ-Metal quantizes the KV cache to int4 on the fly and computes attention directly on the compressed representation via custom Metal compute shaders, eliminating all intermediate dequantization matrices. Across 330 experiments spanning two model families (Gemma 4 31B […]

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arXiv:2603.25883v1 Announce Type: new Abstract: This article revisits the widely studied problem of disinformation and related phenomena in online social networks (OSNs) by reframing it as a broader problem of misrepresentation. While disinformation is commonly understood as the intentional spread of false content, its meaning is applied inconsistently and often remains narrowly content-focused. This obscures other forms of manipulation, such as coordinated behavior that distorts the visibility, popularity or perceived legitimacy of actors and discourses without altering content […]

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arXiv:2601.10832v1 Announce Type: new Abstract: Lower limb exoskeletons and prostheses require precise, real time gait phase and step detections to ensure synchronized motion and user safety. Conventional methods often rely on complex force sensing hardware that introduces control latency. This paper presents a minimalist framework utilizing a single, low cost Inertial-Measurement Unit (IMU) integrated into the crutch hand grip, eliminating the need for mechanical modifications. We propose a five phase classification system, including standard gait phases and a […]

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arXiv:2604.09683v1 Announce Type: new Abstract: Continuous Integration (CI) is widely adopted in modern software development, yet adoption decisions are often made without systematic consideration of project context. Platforms such as GitHub Actions lower the barrier to CI adoption but provide limited support for grounding adoption decisions in project characteristics, leading to redundant services, unmaintained workflows, and costly migrations. Existing research and tooling primarily focus on improving CI after adoption, offering little guidance for assessing suitability before adoption. As […]

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