arXiv:2604.27134v1 Announce Type: new
Abstract: Generative AI is reshaping higher education programming through vibe coding, where students collaborate with AI via natural language rather than writing code line-by-line. We conceptualize this practice …
arXiv:2502.14270v3 Announce Type: replace
Abstract: Birth weight serves as a fundamental indicator of neonatal health, closely linked to both early medical interventions and long-term developmental risks. Traditional predictive models, often constrain…
arXiv:2604.28010v1 Announce Type: new
Abstract: We reframe clinician overrides of clinical AI recommendations as implicit preference data – the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annot…
arXiv:2604.27673v1 Announce Type: cross
Abstract: We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (“Agents”), verbs (“Events”), and objects (“Targets”) from texts. Grounded in cognitive network s…
arXiv:2604.27543v1 Announce Type: new
Abstract: Evaluating English ASR systems for conversational AI applications remains difficult, as many publicly available corpora are either pre-segmented into short segments, consist of read or prepared speech, o…
arXiv:2604.27014v1 Announce Type: new
Abstract: The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learning models. Privacy regulations restrict data sharing, …
arXiv:2604.27092v1 Announce Type: new
Abstract: Scientific research has long been human-led, driving new knowledge and transformative technologies through the continual revision of questions, methods and claims as evidence accumulates. Although large …
arXiv:2604.27467v1 Announce Type: cross
Abstract: Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and evaluation. However, exis…
arXiv:2604.28024v1 Announce Type: new
Abstract: Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw dat…
arXiv:2604.27607v1 Announce Type: new
Abstract: We present JaiTTS-v1.0, a state-of-the-art Thai voice cloning text-to-speech model built through continual training on a large Thai-centric speech corpus. The model architecture is adapted from VoxCPM, a…