Category
arXiv:2602.15873v1 Announce Type: cross Abstract: Tactile-vision-language (TVL) models are increasingly deployed in real-world robotic and multimodal perception tasks, where test-time distribution shifts are unavoidable. Existing …
arXiv:2602.15875v1 Announce Type: cross Abstract: Current Visual-Language Navigation (VLN) methodologies face a trade-off between semantic understanding and control precision. While Multimodal Large Language Models (MLLMs) …
arXiv:2602.15877v1 Announce Type: cross Abstract: Generalized Additive Models (GAMs) balance predictive accuracy and interpretability, but manually configuring their structure is challenging. We propose using the …
arXiv:2602.15878v1 Announce Type: cross Abstract: In industrial scenarios, data augmentation is an effective approach to improve model performance. However, its benefits are not unidirectionally beneficial. …
arXiv:2602.15882v1 Announce Type: cross Abstract: General vision-language models increasingly support unified spatiotemporal reasoning over long video streams, yet deploying such capabilities on robots remains constrained …
arXiv:2602.15888v1 Announce Type: cross Abstract: Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, …
arXiv:2602.15889v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in research both as tools and as objects of investigation. Much of this …
arXiv:2602.15890v1 Announce Type: cross Abstract: This work introduces a neural operator based surrogate modeling framework for neutron transport computation. Two architectures, the Deep Operator Network …
arXiv:2602.15892v1 Announce Type: cross Abstract: Visual perspective taking--inferring how the world appears from another's viewpoint--is foundational to social cognition. We introduce FlipSet, a diagnostic benchmark …
arXiv:2602.15895v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge. However, the inherent discrete representation of text in …
arXiv:2602.15902v1 Announce Type: cross Abstract: Long input sequences are central to in-context learning, document understanding, and multi-step reasoning of Large Language Models (LLMs). However, the …
AbstractOver the last few years, legal scholars, policy-makers, activists and others have generated a vast and rapidly expanding literature concerning the ethical ramifications of using …
