Reasoning models often talk too much, and those extra words can actually make them more wrong.
KARL is a smart search helper that learns to look up information step by step and explain answers using the facts it finds.
This paper says we should test AI the way real life works: by letting it ask questions, gather clues, and make smart moves step by step under a limited budget.
ArtHOI is a new zero-shot method that makes people and everyday articulated objects (like doors, drawers, and fridges) move together realistically using only a single generated video as guidance.
This paper teaches long-horizon AI agents to remember everything exactly without stuffing their whole memory at once.
Proact-VL is a video-talking AI that knows not only what to say but also when to say it, like a great sports commentator.
DREAM is one model that both understands images (like CLIP) and makes images from text (like top text-to-image models).
Large language models can act unpredictably in sensitive places like schools, hospitals, and customer support, so we need reliable ways to guide how they talk and behave.
Reasoning Core is a tool that automatically creates a huge variety of logic and math puzzles, checks every answer with real solvers, and lets you smoothly dial the difficulty up or down.
SageBwd is a way to make the Transformer's attention both fast and trainable by doing most big multiplications in 8-bit instead of full precision.
MMR-Life is a new test (benchmark) that checks how AI understands everyday situations using several real photos at once.
OpenAutoNLU is a simple, open-source tool that automatically builds text understanding models for you.