Science

Researchers have developed Stratum, a system infrastructure designed to efficiently manage and execute large-scale machine learning workflows driven by intelligent agents.

Researchers have successfully demonstrated the viability of soft robotic components in the harsh environment of the stratosphere, opening doors for resilient exploration and adaptable systems.

A new framework combines the power of large language models with structured workflows to accelerate and improve the reliability of identifying potential new medicines.

A new study reveals that while generative AI tools are being embraced by STEM faculty, they’re not necessarily reducing workload-instead, they’re demanding new forms of curation and prompting concerns about how to accurately measure student understanding.

New research explores the complex relationship between scale design, friction, and locomotion in soft robotic snakes.

Researchers have developed a method to train robust agent critics using limited real-world interaction data and a rubric-based supervision system.

Researchers demonstrate that carefully training smaller language models can yield surprisingly powerful results in drug discovery, challenging the current trend of ever-larger AI systems.

New research assesses how well artificial intelligence grasps nuanced social expectations from both text and images.

A new approach to information retrieval empowers deep research agents to find and synthesize data with greater accuracy by explicitly modeling reasoning processes.

As AI agents become increasingly integrated into online platforms, a fundamental problem emerges: we can no longer reliably distinguish between agent-driven and human-driven information seeking.