arXiv:2601.14508v1 Announce Type: new Abstract: Super-time-stepping (STS) methods provide an attractive approach for enabling explicit time integration of parabolic operators, particularly in large-scale, higher-dimensional kinetic simulations where fully implicit schemes are impractical. In this work, we present an explicit STS framework tailored for diffusion operators in gyrokinetic models, motivated by the fact that constructing and storing a Jacobian is often infeasible due to strong nonlocal couplings, high dimensionality, and memory constraints. We investigate the performance of several STS […]

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The problem almost nobody warns you about If you’ve tried training a manipulation policy in Isaac Sim or MuJoCo on assets pulled from Sketchfab, TurboSquid, Objaverse, or your team’s internal CAD library, you’ve probably hit one or more of these: Gripper passes through the object. Object has “infinite mass” and refuses to move. Stacking collapses in bizarre non-physical ways. Contact forces spike to NaN and the sim explodes. Your policy trains to 99% success in sim and faceplants […]

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Individualized decision rules (IDRs) have become increasingly prevalent in societal applications such as personalized marketing, healthcare, and public policy design. However, a critical ethical concern arises from the potential discriminatory effects of IDRs trained on biased data. These algorithms may disproportionately harm individuals from minority subgroups defined by sensitive attributes like gender, race, or language. To address this issue, we propose a novel framework that incorporates demographic parity (DP) and conditional demographic parity (CDP) constraints into the estimation […]

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arXiv:2603.16961v1 Announce Type: new Abstract: The rapid growth of electric vehicles (EVs) requires more effective charging infrastructure planning. Infrastructure layout not only determines deployment cost, but also reshapes charging behavior and influences overall system performance. In addition, destination charging and en-route charging represent distinct charging regimes associated with different power requirements, which may lead to substantially different infrastructure deployment outcomes. This study applies an agent-based modeling framework to generate trajectory-level latent public charging demand under three charging regimes […]

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arXiv:2603.06703v1 Announce Type: new Abstract: Graph databases are widely used in systems that manage rich metadata, yet current modelling practices often embed descriptive attributes directly in nodes, leading to redundancy and inconsistent semantics. This paper introduces the Fifth Graph Normal Form (5GNF), a trait-based normalization framework for property graphs that represents recurring metadata as canonical Trait Nodes connected through HAS_TRAIT relationships. We formalize trait functional dependencies (tFDs) and present the TraitExtraction5GNF algorithm for identifying and extracting reusable traits. […]

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Writing fast GPU code is one of the most grueling specializations in machine learning engineering. Researchers from RightNow AI want to automate it entirely. The RightNow AI research team has released AutoKernel, an open-source framework that applies an autonomous LLM agent loop to GPU kernel optimization for arbitrary PyTorch models. The approach is straightforward: give it any model before you go to bed, and wake up to faster Triton kernels — no GPU expertise required. https://arxiv.org/pdf/2603.21331 Why GPU […]

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arXiv:2601.05329v1 Announce Type: new Abstract: Automatic speech editing aims to modify spoken content based on textual instructions, yet traditional cascade systems suffer from complex preprocessing pipelines and a reliance on explicit external temporal alignment. Addressing these limitations, we propose CosyEdit, an end-to-end speech editing model adapted from CosyVoice through task-specific fine-tuning and an optimized inference procedure, which internalizes speech-text alignment while ensuring high consistency between the speech before and after editing. By fine-tuning on only 250 hours of […]

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arXiv:2603.05686v1 Announce Type: new Abstract: We introduce a perception-related function, OWL, designed to address the complex challenges of 3D perception during motion. It derives its values directly from two fundamental visual motion cues, with one set of cue values per point per time instant. During motion, two visual motion cues relative to a fixation point emerge: 1) perceived local visual looming of points near the fixation point, and 2) perceived rotation of the rigid object relative to the […]

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