arXiv:2604.25711v2 Announce Type: replace-cross
Abstract: Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture developer intent. However, existing vulnerabili…
arXiv:2604.27924v1 Announce Type: cross
Abstract: Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances in large language models (LLMs) have motivated met…
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:2604.27804v1 Announce Type: cross
Abstract: The rapid proliferation of image generation models and other artificial intelligence (AI) systems has intensified concerns regarding data privacy and user consent. As the availability of public dataset…
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:2604.27014v1 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:2604.28024v1 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…