RubricHub is a huge (about 110,000) collection of detailed grading guides (rubrics) for many kinds of questions like health, science, writing, and chat.
UM-Text is a single AI that understands both your words and your picture to add or change text in images so it looks like it truly belongs there.
This paper shows how to make powerful image‑generating Transformers run fast on phones without needing the cloud.
Giving large language models a few good examples and step-by-step instructions can make them much better at spotting feelings in text.
The paper builds a new way to create realistic, long conversations between people and AI that use tools like databases.
The paper introduces Trainee-Bench, a new way to test AI agents that feels like a real first day at work, with tasks arriving over time, hidden clues, and changing priorities.
MemoBrain is like a helpful co-pilot for AI that keeps important thoughts neat and ready so the main thinker (the agent) doesn’t get overwhelmed.
Transformers are powerful but slow because regular self-attention compares every token with every other token, which grows too fast for long sequences.
Large Vision-Language Models (LVLMs) look great on single images but often stumble when they must reason across multiple images.
Computer-using agents kept forgetting important visual details over long tasks and could not reliably find up-to-date, step-by-step help for unfamiliar apps.
This paper teaches AI to build and improve its own small computer helpers (tools) while solving science problems, instead of relying only on a fixed toolbox made beforehand.
TAG-MoE is a new way to steer Mixture-of-Experts (MoE) models using clear task hints, so the right “mini-experts” handle the right parts of an image job.