6 accepted papers
Training data plays a central role in large language model (LLM) optimization, motivating extensive research on data scheduling strategies. Most prior work focuses on data selection and implicitly assumes that, once the training subset is fixed, the order in which data are presented is interchangeab…
Neural Stochastic Differential Equations (Neural SDEs) provide a principled framework for modeling continuous-time stochastic processes and have been widely adopted in fields ranging from physics to finance. Recent advances suggest that Generative Adversarial Networks (GANs) offer a promising soluti…
The annealing stage of Large Language Model (LLM) training is a critical phase where model loss drops sharply and downstream capabilities solidify. Despite its importance, current practices rely on empirical heuristics like quality filtering or context extension, lacking a principled understanding o…
This letter proposes novel wavy soft pneumatic actuators (WSPAs) that integrates an unconstrained base with an elastic chamber of varying wall thicknesses. The base plate design eliminates bottom constraints, thereby enhancing the bending performance of WSPAs. Wavy elastomer cavities with varying wa
Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities. Previous event studies, constrained by static single-companies analyses and simplistic assumptions, fail to capture these ripple effects. While large language models (LLMs) offer emergent reason…
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of hi…