arXiv:2509.16648v1 Announce Type: new Abstract: The accurate trust assessment of multimodal large language models (MLLMs) generated predictions, which can enable selective prediction and improve user confidence, is challenging due to the diverse multi-modal input paradigms. We propose Functionally Equivalent Sampling…
arXiv:2509.00268v3 Announce Type: replace-cross Abstract: Earthquake prediction has long been one of the most elusive challenges in science. Laboratory experiments and simulations suggest that failure precursors should exist, yet reliable signals have remained unobserved in real-world seismic records, leaving open…
arXiv:2509.16656v1 Announce Type: new Abstract: Recent advancements in 2D multimodal large language models (MLLMs) have significantly improved performance in vision-language tasks. However, extending these capabilities to 3D environments remains a distinct challenge due to the complexity of spatial reasoning. Nevertheless,…
arXiv:2509.16298v1 Announce Type: new Abstract: Fuzzy implication functions are one of the most important operators used in the fuzzy logic framework. While their flexible definition allows for diverse families with distinct properties, this variety needs a deeper theoretical understanding of…
arXiv:2509.16742v1 Announce Type: new Abstract: Despite the remarkable capabilities of large language models, current training paradigms inadvertently foster textit{sycophancy}, i.e., the tendency of a model to agree with or reinforce user-provided information even when it’s factually incorrect. To address this…
arXiv:2509.18025v1 Announce Type: cross Abstract: One can see deep-learning models as compositions of functions within the so-called tame geometry. In this expository note, we give an overview of some topics at the interface of tame geometry (also known as o-minimality),…
arXiv:2509.16810v1 Announce Type: new Abstract: Consistent high-quality nursing care is essential for patient safety, yet current nursing education depends on subjective, time-intensive instructor feedback in training future nurses, which limits scalability and efficiency in their training, and thus hampers nursing…
arXiv:2410.19817v3 Announce Type: replace Abstract: Mathematical reasoning has been challenging for large language models (LLMs), and the introduction of step-by-step Chain-of-Thought (CoT) inference has significantly advanced the mathematical capabilities of LLMs. However, current approaches either necessitate extensive inference datasets for…
arXiv:2509.16811v1 Announce Type: new Abstract: Creators struggle to edit long-form, narrative-rich videos not because of UI complexity, but due to the cognitive demands of searching, storyboarding, and sequencing hours of footage. Existing transcript- or embedding-based methods fall short for creative…
arXiv:2506.20608v2 Announce Type: replace Abstract: Generative AI, especially through large language models (LLMs), is transforming how technical knowledge can be accessed, reused, and extended. PETSc, a widely used numerical library for high-performance scientific computing, has accumulated a rich but fragmented…
