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.
The paper teaches AI to write strong research plans by letting it grade its own work using checklists (rubrics) pulled from real scientific papers.
Robust-R1 teaches vision-language models to notice how a picture is damaged, think through what that damage hides, and then answer as if the picture were clear.
Seedance 1.5 pro is a single model that makes video and sound together at the same time, so lips, music, and actions match naturally.
ShowTable is a new way for AI to turn a data table into a beautiful, accurate infographic using a think–make–check–fix loop.
The paper defines Microscopic Spatial Intelligence (MiSI) as the skill AI needs to understand tiny 3D things like molecules from 2D pictures and text, just like scientists do.
MentraSuite is a complete toolkit that teaches large language models (LLMs) to reason about mental health step by step, not just sound caring.
Before this work, most big language models talked one word at a time (autoregressive), which made them slow and hard to parallelize.
EditThinker is a helper brain for any image editor that thinks, checks, and rewrites the instruction in multiple rounds until the picture looks right.
COOPER is a single AI model that both “looks better” (perceives depth and object boundaries) and “thinks smarter” (reasons step by step) to answer spatial questions about images.