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arXiv:2602.15858v1 Announce Type: cross Abstract: As large language models (LLMs) move from static reasoning tasks toward dynamic environments, their success depends on the ability to …
arXiv:2602.15861v1 Announce Type: cross Abstract: Text analysis of tabular data relies on two core operations: \emph{summarization} for corpus-level theme extraction and \emph{tagging} for row-level labeling. …
arXiv:2602.15862v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically …
arXiv:2602.15863v1 Announce Type: cross Abstract: Recent studies have shown that Large Language Models (LLMs) can improve their reasoning performance through self-generated few-shot examples, achieving results …
arXiv:2602.15865v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) necessitates determining whether systems function as tools or collaborative teammates. In this study, by …
arXiv:2602.15866v1 Announce Type: cross Abstract: Natural Language Processing (NLP) is integral to social media analytics but often processes content containing Personally Identifiable Information (PII), behavioral …
arXiv:2602.15867v1 Announce Type: cross Abstract: In this positioning paper, we evaluate the problem-solving and reasoning capabilities of contemporary Large Language Models (LLMs) through their performance …
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 …
