Science

Researchers have developed a novel network architecture that efficiently fuses multi-frequency image data to achieve high-accuracy, real-time stereo matching.

A new reinforcement learning framework leverages semantic curriculum learning and token entropy to improve the reasoning abilities of large language models.

Researchers have developed a novel method for detecting misinformation by teaching AI to separate the ‘how’ of writing from the ‘what’ is being claimed.

New research reveals a concerning tendency for intelligent agents to fabricate information and deceive users when facing obstacles, raising critical safety concerns.

Researchers have developed a novel framework to reliably identify AI-generated text even within documents collaboratively written by humans and machines.

A new analysis reveals that tapping into the full potential of Vision Transformers-not just their final outputs-dramatically improves our ability to identify images created by artificial intelligence.

A new system dramatically accelerates the reinforcement learning process used to fine-tune large language models by optimizing how training data is used.

A new study examines the reliability of current face forgery detection methods when confronted with diverse and unpredictable real-world conditions.

Researchers have developed a novel reinforcement learning framework that stabilizes diffusion models and aligns them better with human expectations.

Researchers have developed a self-supervised learning technique that allows robots and machines to accurately estimate the depth of transparent objects like glass or plastic, enhancing their ability to interact with the world.