Stanford University and Together AI researchers proposed Next-Concept Modeling (NCM), an approach that differs from autoregressive next-token prediction and diffusion-based generation. NCM first generates a coarse concept representation, then predicts finer levels of discrete text codes in parallel before decoding the result. In the paper’s experiments, NCM generated text 10 to 31 times faster than diffusion language-model baselines, with comparable or better quality on the tested tasks. It also outperformed autoregressive and diffusion baselines on difficult Sudoku puzzles with 30 clues, with an 86.1% complete-solution rate compared with 81.3% for FMLM+. The evaluated generator was about 100 million parameters, with experiments extending to 725 million parameters. Performance at larger scales, on exact-text tasks and on long-form generation still requires validation.