Papers8

This paper introduces MM-Lifelong, a 181-hour, multi-scale video dataset designed to test AI on true long-term (lifelong) understanding across days to months.

CoVe is a way to create training conversations for AI agents that use tools, while guaranteeing the conversations are both challenging and correct.

The study tested how an in-car AI helper should talk while it works on long, multi-step tasks.

This report studies the biggest new dangers from super-capable AI and tests them in realistic, well-controlled labs so we can fix problems before they cause real harm.

The paper shows that when we give AI lots of extra text, even harmless extra text, it can get badly confused—sometimes losing up to 80% of its accuracy.

A digital twin is a living computer copy of a real thing (like a bridge, a heart, or a factory) that stays in sync with sensors and helps us predict, fix, and improve the real thing.

This paper organizes how AI agents learn and improve into one simple map with four roads: A1, A2, T1, and T2.

Clinical conversations are special because they mix caring feelings with precise medical facts, and old AI systems struggled to do both at once.