Science

A new framework prioritizes learning progress and efficient resource use, enabling AI agents to pursue knowledge and adapt within realistic constraints.

A new analysis of human-robot interactions reveals that successful navigation in public spaces depends on robots understanding-and participating in-the subtle social cues that govern pedestrian movement.

A new framework ensures reliable coordination in complex multi-agent scenarios by strategically disclosing information, even when agents act conservatively.

A new approach leverages artificial intelligence to provide anatomical pathology technicians with instant, accurate support for complex procedures.

Researchers have developed a novel framework that empowers robots to reliably grasp objects in diverse environments by leveraging the power of latent diffusion models.

New research demonstrates how training deep learning models with a focus on image robustness can overcome technical variations in histopathology, paving the way for more consistent and trustworthy diagnostic tools.

New research reveals that self-propelled particles within liquid crystals can transition from orderly movement to unpredictable, chaotic behavior through dynamic interactions.

A new machine learning framework dramatically speeds up the calculation of how electrons interact with atomic vibrations in complex materials.

New research reveals the key strategies teams are using to maintain productivity and collaboration when working both remotely and in the office.

A new approach to testing vision-language models in robotics reveals hidden behavioral flaws that traditional methods often miss.