Daily AI Paper

Today’s paper tackles a surprisingly hard problem in image generation: not just collecting more data, but organizing data so a model can learn capabilities in the right order. The authors argue that text-to-image synthesis, image editing, and image-knowledge grounding are interconnected skills, and that treating each dataset as an isolated silo leaves performance on the table. Their solution is a capability-driven data infrastructure with specialized data engines for different kinds of supervision, plus a curriculum that gradually changes the mix, quality, concept coverage, and resolution of training data as the model grows. They also add capability-aware evaluation, so weak spots can be detected and the data can be adjusted accordingly. At scale, this produces hundreds of millions of images and pairs, enough to train 3B and 6B diffusion models from scratch. Why it matters is simple: better models often come from better data design, and this paper offers a practical blueprint for building generalist image generators with broader visual coverage and stronger transfer across tasks.