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[[{“value”:”Next-Concept Modeling (NCM) from Stanford’s Hazy Research writes an answer as a rough sketch first and the exact words last, in a handful of passes instead of one pass per token. I ran the released models on a DGX Spark and timed NCM against a same-size word-by-word LLM: it’s up to 4x faster for one request and 4–8x slower at 64 requests. I also trained my own diffusion model, which tied NCM’s 86% on hard Sudoku once it filled the most confident cell first.
Paper: https://arxiv.org/abs/2610.14162
Code: https://github.com/HazyResearch/ncm
Checkpoints: https://huggingface.co/hazyresearch/ncm-checkpoints
Hazy Research blog: https://hazyresearch.stanford.edu/blog/2026-10-12-ncm
Sudoku setup (S-FLM): https://github.com/jdeschena/s-flm
Most-confident-first decoding (Kim et al. 2025): https://arxiv.org/abs/2502.06768
Diffusion Gemma Locally: https://youtu.be/I2-_3ieJelE
Diffusion LLMs Are Here!: https://youtu.be/0B9EMddwlOQ
Optional disclosure: Diffusion Gemma and the Gemini logo appear briefly. If you want the line “I’m joining Google; this is based on public information and my views are my own”, confirm the wording with your recruiter.
Chapters (timed from the final edit)
0:00 What NCM (Next-Concept Modeling) is
0:55 Two questions the paper never tested
1:57 Why LLMs write one token at a time
3:07 Diffusion models: the obvious fix
4:24 How NCM works: coarse-to-fine levels
5:35 What a “concept” is
6:04 The generator: one level per pass
7:06 Paper results: 31x faster, 86% on Sudoku
7:49 What most coverage missed
8:58 Test 1: watching NCM solve Sudoku
10:01 Test 2: NCM vs a same-size LLM
10:38 64 requests: the result flips
12:11 Test 3: my own diffusion Sudoku model
12:40 Most-confident-first ties NCM
13:48 NCM limitations
14:52 Bottom line”}]] Read More Prompt Engineering
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