Papers1055

This paper teaches AI models to reason better by first copying only good examples and later learning from mistakes too.

This paper introduces AV-SpeakerBench, a new test that checks if AI can truly see, hear, and understand who is speaking, what they say, and when they say it in real videos.

Clinical conversations are special because they mix caring feelings with precise medical facts, and old AI systems struggled to do both at once.

RealGen is a new way to make computer-made pictures look so real that they can fool expert detectors and even careful judges.

Before this work, big vision-language models (VLMs) were great at understanding pictures and words together but not at making new pictures.

VQRAE is a new kind of image tokenizer that lets one model both understand images (continuous features) and generate/reconstruct them (discrete tokens).

ThreadWeaver teaches a language model to split big problems into smaller parts it can solve at the same time, like teammates working in parallel.

GEPA is a new way to improve AI prompts by letting the AI read its own work, reflect in plain language on what went wrong, and then rewrite its instructions.

Training a neural network is like finding the lowest spot in a giant, bumpy landscape called the loss landscape.

The paper introduces the Transformer, a model that understands and generates sequences (like sentences) using only attention, without RNNs or CNNs.

This paper teaches computers to understand words by also looking at the smaller pieces inside words, like 'un-', 'play', and '-ing'.