The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors...
Prevailing Vision-Language-Action Models (VLAs) for robotic manipulation are built upon vision-language backbones pretrained on large-scale, but disconnected st...
Semantic communication aims to transmit information most relevant to a task rather than raw data, offering significant gains in communication efficiency for app...
Future AI agents might run autonomously with elevated privileges. If these agents are misaligned, they might abuse these privileges to cause serious damage. The...
Reinforcement learning has become essential for strengthening the reasoning abilities of large language models, yet current exploration mechanisms remain fundam...
This paper presents a unified framework, for the detection, classification, and preliminary localization of anomalies in water distribution networks using multi...
Partial Least Squares (PLS) is a widely used method for data integration, designed to extract latent components shared across paired high-dimensional datasets. ...
With the push towards Exascale computing and data-driven methods, problem sizes have increased dramatically, increasing the computational requirements of the un...
This paper proposes a training data augmentation pipeline that combines synthetic image data with neural style transfer in order to address the vulnerability of...
Large language model (LLM) activations are notoriously difficult to understand, with most existing techniques using complex, specialized methods for interpretin...
Large language models (LLMs) exhibit remarkable capabilities, yet their reasoning remains opaque, raising safety and trust concerns. Attribution methods, which ...
Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions. However, mos...
