arXiv:2510.15969v1 Announce Type: new Abstract: Reformulating nonlinear optimization problems is largely manual and expertise-intensive, yet it remains essential for solving such problems with linear optimization solvers or applying special-purpose algorithms. We introduce textit{LinearizeLLM}, an agent-based framework that solves this task…
arXiv:2510.08010v3 Announce Type: replace Abstract: This work proposes a novel framework based on nested evolving set processes to accelerate Personalized PageRank (PPR) computation. At each stage of the process, we employ a localized inexact proximal point iteration to solve a…
arXiv:2510.15970v1 Announce Type: new Abstract: High-quality training data is the foundation of machine learning and artificial intelligence, shaping how models learn and perform. Although much is known about what types of data are effective for training, the impact of the…
arXiv:2410.01464v4 Announce Type: replace-cross Abstract: Atomic transport underpins the performance of materials in technologies such as energy storage and electronics, yet its simulation remains computationally demanding. In particular, modeling ionic diffusion in solid-state electrolytes (SSEs) requires methods that can overcome…
arXiv:2510.15977v1 Announce Type: new Abstract: Large language models (LLMs) have garnered significant interest in AI community. Despite their impressive generation capabilities, they have been found to produce misleading or fabricated information, a phenomenon known as hallucinations. Consequently, hallucination detection has…
arXiv:2505.10139v3 Announce Type: replace-cross Abstract: Boltzmann Generators have emerged as a promising machine learning tool for generating samples from equilibrium distributions of molecular systems using Normalizing Flows and importance weighting. Recently, Flow Matching has helped speed up Continuous Normalizing Flows…
arXiv:2510.15978v1 Announce Type: new Abstract: Weather prediction is a critical task for human society, where impressive progress has been made by training artificial intelligence weather prediction (AIWP) methods with reanalysis data. However, reliance on reanalysis data limits the AIWPs with…
arXiv:2508.09325v2 Announce Type: replace-cross Abstract: Visual reinforcement learning (RL) is challenging due to the need to extract useful representations from high-dimensional inputs while learning effective control from sparse and noisy rewards. Although large perception models exist, integrating them effectively into…
arXiv:2510.15979v1 Announce Type: new Abstract: Contemporary progress in large language models (LLMs) has revealed notable inferential capacities via reinforcement learning (RL) employing verifiable reward, facilitating the development of O1 and R1-like reasoning models. Directly training from base models with RL…
arXiv:2510.16752v1 Announce Type: cross Abstract: Generative image super-resolution (SR) is rapidly advancing in visual quality and detail restoration. As the capacity of SR models expands, however, so does their tendency to produce artifacts: incorrect, visually disturbing details that reduce perceived…
