Stay updated with the latest research and technology news
arXiv:2602.19004v1 Announce Type: new Abstract: We aim to learn a joint representation between inertial measurement unit (IMU) signals and 2D pose sequences extracted from video,...
arXiv:2602.19005v1 Announce Type: new Abstract: Purpose: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive...
arXiv:2602.19006v1 Announce Type: new Abstract: We present a systematic evaluation of large language models on quantum mechanics problem-solving. Our study evaluates 15 models from five...
arXiv:2602.19007v1 Announce Type: new Abstract: Vector multiplication is a fundamental operation for AI acceleration, responsible for over 85% of computational load in convolution tasks. While...
arXiv:2602.19008v1 Announce Type: new Abstract: Why do language agents fail on tasks they are capable of solving? We argue that many such failures are reliability...
arXiv:2602.19009v1 Announce Type: new Abstract: We study a many-to-one matching model inspired by school choice, where schools evaluate applicants using multiple rankings rather than a...
arXiv:2602.19016v1 Announce Type: new Abstract: Recent advances in LLM based translation have led to renewed interest in fully automated systems, yet professional translators remain essential...
arXiv:2602.19017v1 Announce Type: new Abstract: Theoretical analyses of Empirical Risk Minimization (ERM) are standardly framed within the Real-RAM model of computation. In this setting, training...
arXiv:2602.19019v1 Announce Type: new Abstract: Generative AI models pose a significant challenge to intellectual property (IP), as they can replicate unique artistic styles and concepts...
arXiv:2602.19020v1 Announce Type: new Abstract: Detecting LLM training data is generally framed as a membership inference attack (MIA) problem. However, conventional MIAs operate passively on...
arXiv:2602.19021v1 Announce Type: new Abstract: Large Language Models (LLMs) & Generative AI are transforming cybersecurity, enabling both advanced defenses and new attacks. Organizations now use...
arXiv:2602.19022v1 Announce Type: new Abstract: Accurate sex identification in fish is vital for optimizing breeding and management strategies in aquaculture, particularly for species at the...
arXiv:2602.19024v1 Announce Type: new Abstract: Prompt tuning of large-scale vision-language models such as CLIP enables efficient task adaptation without updating model weights. However, it often...
arXiv:2602.19025v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) offers flexible graph reasoning by combining multiple views of a graph through a learned router. We investigate routing-aware...
arXiv:2602.19027v1 Announce Type: new Abstract: Inverse lithography (ILT) is critical for modern semiconductor manufacturing but suffers from highly non-convex objectives that often trap optimization in...
arXiv:2602.19028v1 Announce Type: new Abstract: This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners' reflexive understanding of...
arXiv:2602.19031v1 Announce Type: new Abstract: The growing computational demands of artificial intelligence (AI) are challenging conventional electronics, making photonic computing a promising alternative. However, existing...
arXiv:2602.19033v1 Announce Type: new Abstract: AI training datasets will inevitably contain AI-generated examples, leading to ``feedback'' in which the output of one model impacts the...
arXiv:2602.19035v1 Announce Type: new Abstract: We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectively...
arXiv:2602.19038v1 Announce Type: new Abstract: Many of the challenges encountered in in-the-wild public deployments of robots remain undocumented despite sharing many common pitfalls. This creates...
arXiv:2602.19040v1 Announce Type: new Abstract: The rise of short-form video platforms and the emergence of multimodal large language models (MLLMs) have amplified the need for...
arXiv:2602.19041v1 Announce Type: new Abstract: A recurring challenge in preference fine-tuning (PFT) is handling $\textit{intransitive}$ (i.e., cyclic) preferences. Intransitive preferences often stem from either $\textit{(i)}$...
arXiv:2602.19043v1 Announce Type: new Abstract: Editing Large language models (LLMs) with real-world, unstructured knowledge is essential for correcting and updating their internal parametric knowledge. In...
arXiv:2602.19046v1 Announce Type: new Abstract: By developing discrete counterparts to recent advances in nonlinear integrability, and in particular to the discovery of explicit formulas, we...