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arXiv:2603.17765v1 Announce Type: cross Abstract: Automated radiology report generation has gained increasing attention with the rise of deep learning and large language models. However, fully …
arXiv:2603.19639v1 Announce Type: new Abstract: Although agentic workflows have demonstrated strong potential for solving complex tasks, existing automated generation methods remain inefficient and underperform, as …
arXiv:2603.19515v1 Announce Type: new Abstract: Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations …
arXiv:2603.19954v1 Announce Type: new Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been …
arXiv:2603.19264v1 Announce Type: cross Abstract: With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to …
arXiv:2603.19263v1 Announce Type: cross Abstract: This study examined how high school students with different motivational profiles use generative AI tools in math and writing. Through …
arXiv:2603.19782v1 Announce Type: new Abstract: Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pursuit governed …
arXiv:2603.19260v1 Announce Type: cross Abstract: Sign Language Machine Translation (SLMT) aims to bridge communication between Deaf and hearing individuals. However, its progress is constrained by …
arXiv:2603.19584v1 Announce Type: new Abstract: Battery life remains a critical challenge for mobile devices, yet existing power management mechanisms rely on static rules or coarse-grained …
arXiv:2603.19500v1 Announce Type: new Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal …
arXiv:2603.19265v1 Announce Type: cross Abstract: This paper investigates the ontological consequences of fine-tuning Large Language Models (LLMs) on "impossible objects" -- entities defined by mutually …
arXiv:2603.19266v1 Announce Type: cross Abstract: Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite …
