Science

As AI increasingly automates scientific software development, ensuring its outputs align with established knowledge and rigorous standards is paramount.

New research shows that artificial intelligence agents interacting on visual social networks develop and maintain distinct aesthetic preferences, defying expectations of rapid cultural convergence.

New machine learning techniques are helping researchers pinpoint moments of mechanistic reasoning within classroom discussions.

A new review explores the challenges of enabling stable locomotion for legged robots operating in dynamic, moving environments.

A growing chorus of researchers argues that unlocking the full potential of deep learning demands a fundamental shift from empirical observation to a rigorous, mechanistic understanding of its inner workings.

A novel framework enables the simultaneous optimization of robot design, fleet composition, and task planning for complex, real-world scenarios.

A new approach leverages artificial intelligence agents to automate the optimization of complex detector systems, promising faster progress in high-energy physics.

A new study demonstrates how interactive, language-driven robots can effectively raise robotics awareness among non-expert users in a corporate environment.

A new agentic architecture is streamlining the scientific process by automatically translating research goals into fully executable workflows.

A new approach uses large language models to formalize safety protocols and redundant systems to ensure reliable operation in human-robot workcells.