arXiv:2508.05498v2 Announce Type: replace
Abstract: Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) techniques have exhibited remarkable performance across a wide range of domains. However, existing RAG approaches pri…
arXiv:2604.19394v1 Announce Type: new
Abstract: This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via continual pre-training and merging. We address the s…
arXiv:2604.18912v1 Announce Type: new
Abstract: Discovering optimal designs through sequential data collection is essential in many real-world applications. While Bayesian Optimization (BO) has achieved remarkable success in this setting, growing atte…
arXiv:2604.19522v1 Announce Type: new
Abstract: Bimanual mobile manipulation requires a seamless integration between high-level semantic reasoning and safe, compliant physical interaction – a challenge that end-to-end models approach opaquely and clas…
arXiv:2604.19292v1 Announce Type: cross
Abstract: Multilingual large language models (LLMs) have minimized the fluency gap between languages. This advancement, however, exposes models to the risk of biased behavior, as knowledge and norms may propagat…
arXiv:2604.19133v1 Announce Type: new
Abstract: Robust 3D reconstruction across varying environmental conditions remains a critical challenge for robotic perception, particularly when transitioning between air and water. To address this, we introduce …
arXiv:2604.18936v1 Announce Type: new
Abstract: Despite the growing application of Large Language Models (LLMs) to theoretical physics, there is little academic exploration into how domain-specific physics reasoning ability develops while training the…
arXiv:2604.19536v1 Announce Type: new
Abstract: Recent navigation systems achieve strong benchmark results, yet real-world deployment often remains visibly stop-and-go. This bottleneck arises because the sense-inference-execution loop is still blockin…
arXiv:2604.19405v1 Announce Type: new
Abstract: Automatic evaluators such as reward models play a central role in the alignment and evaluation of large vision-language models (LVLMs). Despite their growing importance, these evaluators are almost exclu…
arXiv:2602.04749v2 Announce Type: replace
Abstract: Long-tailed class imbalance remains a fundamental obstacle in semantic segmentation of high-resolution remote-sensing imagery, where dominant classes shape learned representations and rare classes ar…