LatentMorph teaches an image-making AI to quietly think in its head while it draws, instead of stopping to write out its thoughts in words.
This paper fixes a common problem in reasoning AIs called Lazy Reasoning, where the model rambles instead of making a good plan.
TRIP-Bench is a new test that checks if AI travel agents can plan real trips over many chat turns while following strict rules and changing user requests.
CoDiQ is a recipe for making hard-but-solvable math and coding questions on purpose, and it controls how hard they get while you generate them.
This paper teaches multimodal AI models to not just read pictures but to also imagine and think with pictures inside their heads.
PromptRL teaches a language model to rewrite prompts while a flow-based image model learns to draw, and both are trained together using the same rewards.
Large language models sometimes reach the right answer for the wrong reasons, which is risky and confusing.
The paper tackles a real problem: one-shot image or text searches often miss the right evidence (low hit-rate), especially in noisy, cluttered pictures.
MMFineReason is a huge, open dataset (1.8 million examples, 5.1 billion solution tokens) that teaches AIs to think step by step about pictures and text together.
The paper shows how to make AI think faster and smarter by planning in a hidden space instead of writing long step-by-step sentences.
ASTRA is a fully automated way to train tool-using AI agents by making both their practice stories (trajectories) and their practice worlds (environments) without humans in the loop.
MemOCR is a new way for AI to remember long histories by turning important notes into a picture with big, bold parts for key facts and tiny parts for details.