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arXiv:2603.18189v1 Announce Type: new Abstract: Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely …
arXiv:2603.18085v1 Announce Type: new Abstract: Recent incidents have highlighted alarming cases where human-AI interactions led to negative psychological outcomes, including mental health crises and even …
arXiv:2603.18331v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved remarkable empirical success, yet the absence of a principled theoretical foundation continues to hinder …
arXiv:2603.18729v1 Announce Type: new Abstract: Many works in the literature show that LLM outputs exhibit discriminatory behaviour, triggering stereotype-based inferences based on the dialect in …
arXiv:2603.18472v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success in interpreting natural scenes, their ability to process discrete symbols …
arXiv:2603.18577v1 Announce Type: new Abstract: Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and …
arXiv:2603.18015v1 Announce Type: new Abstract: Although automated harmful content detection systems are frequently used to monitor online platforms, moderators and end users frequently cannot understand …
arXiv:2603.18018v1 Announce Type: new Abstract: The natural language to SQL (NL2SQL) task plays a pivotal role in democratizing data access by enabling non-expert users to …
arXiv:2603.18290v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and …
arXiv:2603.18813v1 Announce Type: new Abstract: This paper is the second one in a series of work on the mathematical creativity of LLM. In the first …
arXiv:2603.18761v1 Announce Type: new Abstract: Standard attention mechanisms in transformers are limited by their pairwise formulation, which hinders the modeling of higher-order dependencies among tokens. …
arXiv:2603.18388v1 Announce Type: new Abstract: Automatic prompt optimization (APO) has emerged as a powerful paradigm for improving LLM performance without manual prompt engineering. Reflective APO …