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arXiv:2506.01062v3 Announce Type: replace Abstract: We introduce SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields conflicting,...
arXiv:2506.01941v3 Announce Type: replace Abstract: Enabling robots with contact-rich manipulation remains a pivotal challenge in robot learning, which is substantially hindered by the data collection...
arXiv:2506.02015v3 Announce Type: replace Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled unified multimodal understanding and generation. However, they still struggle with...
arXiv:2506.03067v3 Announce Type: replace Abstract: Text-to-image generation models~(e.g., Stable Diffusion) have achieved significant advancements, enabling the creation of high-quality and realistic images based on textual...
arXiv:2506.03938v2 Announce Type: replace Abstract: New UAV technologies and the NewSpace era are transforming Earth Observation missions and data acquisition. Numerous small platforms generate large...
arXiv:2506.04764v3 Announce Type: replace Abstract: Visual environments are inherently hierarchical, as a panoramic view naturally encompasses and organizes multiple perspective views within its field. Capturing...
arXiv:2506.06683v3 Announce Type: replace Abstract: Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios.While existing methods have achieved promising...
arXiv:2506.07080v2 Announce Type: replace Abstract: The growing availability of high-quality Earth Observation (EO) data enables accurate global land cover and crop type monitoring. However, the...
arXiv:2506.07915v2 Announce Type: replace Abstract: Autonomous systems increasingly receive time-sensitive contextual updates from humans through natural language, yet embedding language understanding inside decision-makers couples grounding...
arXiv:2506.08618v3 Announce Type: replace Abstract: AI is transforming scientific research by revealing new ways to understand complex physical systems, but its impact remains constrained by...
arXiv:2506.08921v2 Announce Type: replace Abstract: We consider the problem of propagating the uncertainty from a possibly large number of random inputs through a computationally expensive...
arXiv:2506.09016v3 Announce Type: replace Abstract: Training large language models with reinforcement learning (RL) against verifiable rewards significantly enhances their reasoning abilities, yet remains computationally expensive...
arXiv:2506.09984v2 Announce Type: replace Abstract: End-to-end human animation with rich multi-modal conditions, e.g., text, image and audio has achieved remarkable advancements in recent years. However,...
arXiv:2506.14020v4 Announce Type: replace Abstract: Graph generation has emerged as a critical task in fields ranging from drug discovery to circuit design. Contemporary approaches, notably...
arXiv:2506.14067v3 Announce Type: replace Abstract: As interactive generative systems are increasingly deployed in real-world applications, their tendency to generate unreliable or false responses raises serious...
arXiv:2506.16112v3 Announce Type: replace Abstract: Inspired by text prompts in large language models, visual prompts have been explored to enhance the perceptual capabilities of large...
arXiv:2506.18339v3 Announce Type: replace Abstract: Understanding and modeling nonlinear dynamical systems is a fundamental challenge across science and engineering. Deep learning has shown remarkable potential...
arXiv:2506.18812v2 Announce Type: replace Abstract: Physics-informed deep learning has achieved remarkable progress by embedding geometric priors, such as Hamiltonian symmetries and variational principles, into neural...
arXiv:2506.23036v3 Announce Type: replace Abstract: This paper explores reinforcement learning (RL) policy robustness by systematically analyzing network parameters under internal and external stresses. \textcolor{black}{We apply...
arXiv:2506.23508v4 Announce Type: replace Abstract: Post-training algorithms such as Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) are widely used to adapt (multimodal) large language models...
arXiv:2507.00091v2 Announce Type: replace Abstract: We consider a coded distributed computing problem in a ring-based communication network, where $N$ computing nodes are arranged in a...
arXiv:2507.00677v2 Announce Type: replace Abstract: We present an imitation learning framework that extracts distinctive legged locomotion behaviors and transitions between them from unlabeled real-world motion...
arXiv:2507.01785v3 Announce Type: replace Abstract: Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on...
arXiv:2507.01853v5 Announce Type: replace Abstract: The rapid evolution of Large Language Models' has underscored the need for evaluation frameworks that are globally applicable, flexible, and...