Science

Researchers have achieved nanoscale nuclear magnetic resonance using a silicon carbide-based spin sensor, opening doors to atomic-resolution imaging of molecular structures.

A new reinforcement learning algorithm, Symphony, dramatically improves the sample efficiency and safety of humanoid robot locomotion.

A new machine learning framework, ID-PaS, dramatically improves the performance of a key optimization technique by learning from the structure of mathematical problems.

A new review explores how incorporating specialized knowledge can significantly improve the effectiveness of automated software testing, particularly for complex cyber-physical systems.

Researchers have developed a framework that translates natural language descriptions into lifelike human movements, offering a significant advance over video-based approaches.

New research reveals how we can visualize and understand the complex, self-referential processes unfolding within large language models.

New research demonstrates how deep reinforcement learning can dramatically improve emergency braking systems and reduce harm in multi-vehicle collisions.

A new axiomatic system formally defines the core components of learning analytics, providing a foundation for rigorous research and practical application.

Researchers have developed a novel method for training agents using both labeled actions and unlabeled trajectories, significantly reducing the need for costly and time-consuming data annotation.

New modeling reveals how future telescopes might detect key atmospheric gases indicative of life on Earth-like exoplanets.