arXiv:2512.10427v3 Announce Type: replace Abstract: Neural scaling laws and double-descent phenomena suggest that deep-network training obeys a simple macroscopic structure despite highly nonlinear optimization dynamics. We derive such structure directly from gradient descent in function space. For mean-squared error loss,…
arXiv:2512.18104v1 Announce Type: new Abstract: Aleatoric uncertainties – irremovable variability in microstructure morphology, constituent behavior, and processing conditions – pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational Deep Material Network (VDMN), a physics-informed surrogate model…
arXiv:2504.01570v2 Announce Type: replace-cross Abstract: With the aim of generalizing histogram statistics to higher dimensional cases, density estimation via discrepancy based sequential partition (DSP) has been proposed to learn an adaptive piecewise constant approximation defined on a binary sequential partition…
arXiv:2512.18120v1 Announce Type: new Abstract: Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}$, an inverse basis-to-basis neural operator framework that addresses this limitation. Our key innovation is to…
arXiv:2511.21728v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled fluent dialogue systems, but most remain reactive and struggle in emotionally rich, goal-oriented settings such as marketing conversations. To address this limitation, we propose AffectMind, a…
arXiv:2512.18129v1 Announce Type: new Abstract: Survival analysis is a critical tool for modeling time-to-event data. Recent deep learning-based models have reduced various modeling assumptions including proportional hazard and linearity. However, a persistent challenge remains in incorporating longitudinal covariates, with prior…
arXiv:2512.19027v1 Announce Type: cross Abstract: Developers often struggle to specify correct training labels and rewards. Perhaps they don’t need to. We propose recontextualization, which reduces how often language models “game” training signals, performing misbehaviors those signals mistakenly reinforce. We show…
arXiv:2512.18133v1 Announce Type: new Abstract: Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming more professional in real-world scenarios, fraudsters are employing more…
arXiv:2512.19366v1 Announce Type: cross Abstract: While reinforcement learning methods have delivered remarkable results in a number of settings, generalization, i.e., the ability to produce policies that generalize in a reliable and systematic way, has remained a challenge. The problem of…
arXiv:2512.18174v1 Announce Type: new Abstract: The current era of AI development places a heavy emphasis on training large models on increasingly scaled-up datasets. This paradigm has catalyzed entirely new product categories, such as LLM chatbots, while also raising concerns about…
