The paper teaches AI to write strong research plans by letting it grade its own work using checklists (rubrics) pulled from real scientific papers.
ProGuard is a safety guard for text and images that doesn’t just spot known problems—it can also recognize and name new, never-seen-before risks.
MindWatcher is a smart AI agent that can think step by step and decide when to use tools like web search, image zooming, and a code calculator to solve tough, multi-step problems.
The paper asks which small, add-on training tricks (PEFT) work best when we teach language models with yes/no rewards we can check (RLVR).
The paper teaches vision-language models (AIs that look and read) to pay attention to the right picture parts without needing extra tools during answering time.
MAI-UI is a family of AI agents that can see, understand, and control phone and computer screens using plain language.
SmartSnap teaches an agent not only to finish a phone task but also to prove it with a few perfect snapshots it picks itself.
This paper teaches AI to notice not just what is in a picture, but how the picture looks and feels to people.
Nemotron 3 is a new family of open AI models (Nano, Super, Ultra) built to think better while running faster and cheaper.
LongVideoAgent is a team of three AIs that work together to answer questions about hour‑long TV episodes without missing small details.
This paper builds DiRL, a fast and careful way to finish training diffusion language models so they reason better.
This paper adds a tiny but powerful step called Early Knowledge Alignment (EKA) to multi-step retrieval systems so the model takes a quick, smart look at relevant information before it starts planning.