Unified Thinker separates “thinking” (planning) from “drawing” (image generation) so complex instructions get turned into clear, doable steps before any pixels are painted.
Talk2Move is a training recipe that lets an image editor move, rotate, and resize the exact object you mention using plain text, while keeping the rest of the picture stable.
WebGym is a giant practice world (almost 300,000 tasks) that lets AI web agents learn on real, ever-changing websites instead of tiny, fake ones.
This paper teaches AI to solve diagram-based math problems by copying how people think: first see (perception), then make sense of what you saw (internalization), and finally reason (solve the problem).
DreamID-V is a new AI method that swaps faces in videos while keeping the body movements, expressions, lighting, and background steady and natural.
The paper teaches small language models to predict open-ended future events by turning daily news into thousands of safe, graded practice questions.
This paper builds an open, end-to-end ecosystem (ALE) that lets AI agents plan, act, and fix their own mistakes across many steps in real computer environments.
Dream2Flow lets a robot watch a short, AI-generated video of a task and then do that task in real life by following object motion in 3D.
Youtu-LLM is a small (1.96B) language model that was trained from scratch to think, plan, and act like an agent instead of just copying bigger models.
Youtu-Agent is a build-and-grow factory for AI agents that cuts manual setup and keeps agents improving over time.
SenseNova-MARS is a vision-language model that can think step-by-step and use three tools—text search, image search, and image cropping—during its reasoning.
Multimodal Large Language Models (MLLMs) often hallucinate on videos by trusting words and common sense more than what the frames really show.