V-Retrver is a new way for AI to search across text and images by double-checking tiny visual details instead of only guessing from words.
LIVE is a new way to train video-making AIs so their mistakes don’t snowball over long videos.
When rewards are rare, a popular training method for language models (GRPO) often stops learning because every try in a group gets the same score, so there is nothing to compare.
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.
TTCS is a way for a model to teach itself during the test by first making easier practice questions that are similar to the real hard question and then learning from them.
This paper teaches a model to be its own teacher so it can climb out of a learning plateau on very hard math problems.
DARC teaches big language models to get smarter by splitting training into two calm, well-organized steps instead of one chaotic loop.
ShapeR builds clean, correctly sized 3D objects from messy, casual phone or glasses videos by using images, camera poses, sparse SLAM points, and short text captions together.
Dr. Zero is a pair of AI agents (a Proposer and a Solver) that teach each other to do web-search-based reasoning without any human-written training data.
Solar Open is a giant bilingual AI (102 billion parameters) that focuses on helping underserved languages like Korean catch up with English-level AI quality.
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.
DreamID-V is a new AI method that swaps faces in videos while keeping the body movements, expressions, lighting, and background steady and natural.