March 2026

arXiv:2603.24826v1 Announce Type: new Abstract: Synthetic data generation through document rewriting has emerged as a promising technique for improving language model pretraining, yet most studies focus on English and do not systematically control for the quality of the source data being rewritten. We present a controlled study of how synthetic rewriting interacts with source data quality in the context of Portuguese continued pretraining. Starting from ClassiCC-PT, a Portuguese corpus annotated with STEM and Educational quality scores, we construct […]

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arXiv:2603.24825v1 Announce Type: new Abstract: Patch reviewing is critical for software development, especially in distributed open-source development, which highly depends on voluntary work, such as Linux. This paper studies the past 10 years of patch reviews of the Linux memory management subsystem to characterize the challenges involved in patch reviewing at scale. Our study reveals that the review process is still primarily reliant on human effort despite a wide-range of automatic checking tools. Although kernel developers strive to […]

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arXiv:2603.24823v1 Announce Type: new Abstract: We formalize Hilbert’s Seventh Problem and its solution, the Gelfond-Schneider theorem, in the Lean 4 proof assistant. The theorem states that if $alpha$ and $beta$ are algebraic numbers with $alpha neq 0,1$ and $beta$ irrational, then $alpha^beta$ is transcendental. Originally proven independently by Gelfond and Schneider in 1934, this result is a cornerstone of transcendental number theory, bridging algebraic number theory and complex analysis.

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arXiv:2603.24821v1 Announce Type: new Abstract: State-of-the-art crowd counting and localization are primarily modeled using two paradigms: density maps and point regression. Given the field’s security ramifications, there is active interest in model robustness against adversarial attacks. Recent studies have demonstrated transferability across density-map-based approaches via adversarial patches, but cross-paradigm attacks (i.e., across both density map-based models and point regression-based models) remain unexplored. We introduce a novel adversarial framework that compromises both density map and point regression architectural paradigms […]

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arXiv:2603.24819v1 Announce Type: new Abstract: We propose Weak and Entropy PINNs (WE-PINNs) for the approximation of entropy solutions to nonlinear hyperbolic conservation laws. Standard physics-informed neural networks enforce governing equations in strong differential form, an approach that becomes structurally inconsistent in the presence of discontinuities due to the divergence of strong-form residuals near shocks. The proposed method replaces pointwise residual minimization with a space–time weak formulation derived from the divergence theorem. Conservation is enforced through boundary flux integrals […]

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arXiv:2603.24815v1 Announce Type: new Abstract: Pin sites represent the interface where a metal pin or wire from the external environment passes through the skin into the internal environment of the limb. These pins or wires connect an external fixator to the bone to stabilize the bone segments in a patient with trauma or deformity. Because these pin sites represent an opportunity for external skin flora to enter the internal environment of the limb, infections of the pin site […]

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arXiv:2603.24813v1 Announce Type: new Abstract: This paper presents an online path planning algorithm for safe autonomous manipulation of a flexibly constrained object in an unknown environment. Methods for real time identification and characterization of perceived flexible constraints and global stiffness are presented. Used in tandem, these methods allow a robot to simultaneously explore, characterize, and manipulate an elastic system safely. Navigation without a-priori knowledge of the system is achieved using constraint exploration based on local force and position […]

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arXiv:2603.24812v1 Announce Type: new Abstract: Numerical software depends on fast, accurate implementations of mathematical primitives like sin, exp, and log. Modern superoptimizers can optimize floating-point kernels against a given set of such primitives, but a more fundamental question remains open: which new primitives are worth implementing in the first place? We formulate this as numerical library learning: given a workload of floating-point kernels, identify the mathematical primitives whose expert implementations would most improve speed and accuracy. Our key […]

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arXiv:2603.24811v1 Announce Type: new Abstract: Scalable control of pneumatic and fluidic networks remains fundamentally constrained by architectures that require continuous power input, dense external control hardware, and fixed routing topologies. Current valve arrays rely on such continuous actuation and mechanically fixed routing, imposing substantial thermal and architectural overhead. Here, we introduce the Switchable-polarity ElectroPermanent Magnet (S-EPM), a fundamentally new bistable magnetic architecture that deterministically reverses its external magnetic polarity through transient electrical excitation. By reconfiguring internal flux pathways […]

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arXiv:2603.24806v1 Announce Type: new Abstract: Diffusion models are increasingly used for robot learning, but current designs face a clear trade-off. Action-chunking diffusion policies like ManiCM are fast to run, yet they only predict short segments of motion. This makes them reactive, but unable to capture time-dependent motion primitives, such as following a spring-damper-like behavior with built-in dynamic profiles of acceleration and deceleration. Recently, Movement Primitive Diffusion (MPD) partially addresses this limitation by parameterizing full trajectories using Probabilistic Dynamic […]

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