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arXiv:2602.03289v1 Announce Type: new Abstract: Continuing previous work, this paper focuses on the summability problem of multivariate rational functions in the mixed case in which...
arXiv:2602.03290v1 Announce Type: new Abstract: We study universal approximation of continuous functionals on compact subsets of products of Hilbert spaces. We prove that any such...
arXiv:2602.03292v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) offers a practical solution for deploying image segmentation models under domain shift without accessing source data or...
arXiv:2602.03293v1 Announce Type: new Abstract: Unsupervised anomaly detection stands as an important problem in machine learning, with applications in financial fraud prevention, network security and...
arXiv:2602.03294v1 Announce Type: new Abstract: Accurate, infrastructure-less sensor systems for motion tracking are essential for mobile robotics and augmented reality (AR) applications. The most popular...
arXiv:2602.03295v1 Announce Type: new Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated remarkable capabilities. However, their deployment is hindered by significant computational...
arXiv:2602.03297v1 Announce Type: new Abstract: Deep equilibrium models (DEQs) achieve infinitely deep network representations without stacking layers by exploring fixed points of layer transformations in...
arXiv:2602.03300v1 Announce Type: new Abstract: In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs...
arXiv:2602.03301v1 Announce Type: new Abstract: In reinforcement learning (RL), Q-learning is a fundamental algorithm whose convergence is guaranteed in the tabular setting. However, this convergence...
arXiv:2602.03302v1 Announce Type: new Abstract: Optical coherence tomography (OCT) has revolutionized retinal disease diagnosis with its high-resolution and three-dimensional imaging nature, yet its full diagnostic...
arXiv:2602.03304v1 Announce Type: new Abstract: Deep search agents, which autonomously iterate through multi-turn web-based reasoning, represent a promising paradigm for complex information-seeking tasks. However, current...
arXiv:2602.03305v1 Announce Type: new Abstract: Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by...
arXiv:2602.03306v1 Announce Type: new Abstract: Dense retrieval represents queries and docu-002 ments as high-dimensional embeddings, but003 these representations can be redundant at the004 query level:...
arXiv:2602.03307v1 Announce Type: new Abstract: Audio foundation models learn general-purpose audio representations that facilitate a wide range of downstream tasks. While the performance of these...
arXiv:2602.03309v1 Announce Type: new Abstract: Hybrid training methods for large language models combine supervised fine tuning (SFT) on expert demonstrations with reinforcement learning (RL) on...
arXiv:2602.03310v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models hold promise for generalist robotics but currently struggle with data scarcity, architectural inefficiencies, and the inability to...
arXiv:2602.03311v1 Announce Type: new Abstract: Conversational AI systems combine AI-based solutions with the flexibility of conversational interfaces. However, most existing testing solutions do not straightforwardly...
arXiv:2602.03314v1 Announce Type: new Abstract: Defect depth quantification in additively manufactured (AM) components remains a significant challenge for non-destructive testing (NDT). This study proposes a...
arXiv:2602.03315v1 Announce Type: new Abstract: Agent memory systems must accommodate continuously growing information while supporting efficient, context-aware retrieval for downstream tasks. Abstraction is essential for...
arXiv:2602.03316v1 Announce Type: new Abstract: With the rapid advancement of image generative models, generative data augmentation has become an effective way to enrich training images,...
arXiv:2602.03318v1 Announce Type: new Abstract: Operations Research (OR) relies on expert-driven modeling-a slow and fragile process ill-suited to novel scenarios. While large language models (LLMs)...
arXiv:2602.03319v1 Announce Type: new Abstract: Target-oriented discovery under limited evaluation budgets requires making reliable progress in high-dimensional, heterogeneous design spaces where each new measurement is...
arXiv:2602.03320v1 Announce Type: new Abstract: Medical image segmentation is evolving from task-specific models toward generalizable frameworks. Recent research leverages Multi-modal Large Language Models (MLLMs) as...
arXiv:2602.03322v1 Announce Type: new Abstract: We consider a nonlinear Klein--Gordon equation in the nonrelativistic limit regime with initial data in the form of a modulated...