Rui Xin

Turing-20B-A2B achieves exceptional long-context capabilities with only a fraction of its parameters activated, pushing the boundaries of efficiency in physical AI applications.

A new benchmark reveals that existing defect detection methods falter in novel production scenarios, highlighting the urgent need for severity-aware assessments in manufacturing.

TuringViT outperforms leading ViT models with just 10% of the training data, making state-of-the-art vision technology accessible to a wider audience.

Injecting diversity at the specification level can significantly enhance output variety while preserving quality, challenging conventional methods that often fall short.