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arXiv:2602.13835v1 Announce Type: new Abstract: Sound designers search for sounds in large sound effects libraries using aspects such as sound class or visual context. However,...
arXiv:2602.13836v1 Announce Type: new Abstract: Speculative decoding has rapidly emerged as a leading approach for accelerating language model (LM) inference, as it offers substantial speedups...
arXiv:2602.13837v1 Announce Type: new Abstract: We introduce a video diffusion model for high-fidelity, causal, and real-time video generation under ultra-low-bitrate semantic communication constraints. Our approach...
arXiv:2602.13840v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy violations may arise...
arXiv:2602.13841v1 Announce Type: new Abstract: We present a monolithic hp space-time multigrid method (hp-STMG) for tensor-product space-time finite element discretizations of the incompressible Navier-Stokes equations....
arXiv:2602.13842v1 Announce Type: new Abstract: Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI)....
arXiv:2602.13843v1 Announce Type: new Abstract: We present an anisotropic goal-oriented error estimator based on the Dual Weighted Residual (DWR) method for time-dependent convection-dominated problems. Using...
arXiv:2602.13844v1 Announce Type: new Abstract: This paper presents a comprehensive workflow for generating and validating a synthetic dataset designed for robotic surgery instrument segmentation. A...
arXiv:2602.13845v1 Announce Type: new Abstract: [Context] Online Recruitment and Selection (R&S) processes are often the first point of contact between early-career software engineers and the...
arXiv:2602.13846v1 Announce Type: new Abstract: Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires...
arXiv:2602.13848v1 Announce Type: new Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a...
arXiv:2602.13849v1 Announce Type: new Abstract: Efficient tabletop rearrangement remains challenging due to collisions and the need for temporary buffering when target poses are obstructed. Prehensile...
arXiv:2602.13850v1 Announce Type: new Abstract: We investigate a skill-based framework for humanoid box rearrangement that enables long-horizon execution by sequencing reusable skills at the task...
arXiv:2602.13851v1 Announce Type: new Abstract: Formal specifications are crucial for building verifiable and dependable software systems, yet generating accurate and verifiable specifications for real-world C...
arXiv:2602.13852v1 Announce Type: new Abstract: Modern online experimentation faces two bottlenecks: scarce traffic forces tough choices on which variants to test, and post-hoc insight extraction...
arXiv:2602.13855v1 Announce Type: new Abstract: A deep research agent produces a fluent scientific report in minutes; a careful reader then tries to verify the main...
arXiv:2602.13857v1 Announce Type: new Abstract: Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices,...
arXiv:2602.13859v1 Announce Type: new Abstract: Despite their impressive performance on computer vision benchmarks, Deep Neural Networks (DNNs) still fall short of adequately modeling human visual...
arXiv:2602.13860v1 Announce Type: new Abstract: The overarching research direction of this work is the development of a ''Responsible Intelligence'' framework designed to reconcile the immense...
arXiv:2602.13861v1 Announce Type: new Abstract: We introduce a variant of the multiway cut that we call the min-max connected multiway cut. Given a graph $G=(V,E)$...
arXiv:2602.13864v1 Announce Type: new Abstract: Learning in the presence of missing data can result in biased predictions and poor generalizability, among other difficulties, which data...
arXiv:2602.13865v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) frameworks like Option-Critic (OC) and Multi-updates Option Critic (MOC) have introduced significant advancements in learning reusable...
arXiv:2602.13866v1 Announce Type: new Abstract: Deploying heterogeneous robot teams to accomplish multiple tasks over extended time horizons presents significant computational challenges for task allocation and...
arXiv:2602.13867v1 Announce Type: new Abstract: Large language models (LLMs) are being deployed across the Global South, where everyday use involves low-resource languages, code-mixing, and culturally...