Stanford just reinvented text generation. (it’s neither autoregressive nor diffusion) Autoregressive LLMs generate one token at a time. Longer answers require longer chains of sequential predictions. Diffusion LLMs generate many tokens in parallel, but still need repeated refinement passes. Each pass processes the full sequence, and quality drops quickly when you cut the passes. Stanford researchers proposed another approach, Next-Concept Modeling (NCM). It generates text from coarse structure to fine detail in roughly a dozen passes. First, a tokenizer learns to represent text as layers of compressed information. Early layers capture broad structure, while later layers add details the earlier ones missed. A transformer then learns to generate these layers in order, predicting everything within each layer simultaneously. A decoder converts the completed representation into text. The advantage is that early layers are tiny. Generation needs fewer passes, and those early passes process much less information. You can see this in the team’s translation example. Early layers produce rough, repetitive text, which later layers refine into a clean translation. In the researchers’ tests, NCM generated text 10–31× faster than strong diffusion baselines, with comparable or better benchmark scores. On LM1B and OpenWebText, it also used roughly 4–5× fewer training tokens, including tokenizer training. That speed can also improve quality. Generating several candidates and selecting one based on agreement outperformed diffusion baselines at the same sampling time budget. The limits matter. Scaling experiments reach 725M parameters, tasks use separate tokenizers, and the release includes evaluation code and checkpoints without a training pipeline. Some evaluations also use repetition penalties during final decoding, without additional model calls. NCM shows that parallel generation can work by building increasingly detailed representations. Whether those gains hold at general-purpose LLM scale remains open. - GitHub repo: github.com/HazyResearch/ncm - Paper: arxiv.org/abs/2610.14162

Google just open-sourced Harness². (turn any harness into a self-improving system) a harness is everything wrapped around the model, like the system prompt, tools, memory, skills, and subagents. today a person designs it once, and the agent runs every new task with that same fixed setup. Harness² lets the agent refine that setup for each new task on its own, with the model weights left untouched. on every task, the agent proposes edits to its harness, runs a few candidate versions, and composes a better harness by comparing how those runs behaved. because it learns from the differences between runs, it needs no correct answer or grader for the task. that covers a single task. Harness² also carries what it learns from one task into the next. after each task, it saves the harness it settled on along with the lessons that held up across the runs. the next time it refines a harness, it starts from those notes instead of from scratch. this means every finished task makes the next round of edits more informed. Google tested this with Gemini 3.5 Flash and GLM-5 on legal, office, and professional work benchmarks. one round of edits was enough to beat their original harnesses, and giving the agent more rounds pushed scores higher. those extra rounds can go wide or deep. the agent either tries several candidate harnesses at once or keeps refining a single one step by step, using OpenCode or Codex to carry out the work. you can find the Harness² repo here: github.com/google-research/h… I wrote about a similar idea for agents already running in production, where the harness should repair itself after a failure instead of waiting for someone to debug the trace by hand. that piece walks through how @Cometml's Opik (open-source) closes this loop, going from a failing trace to a proposed fix to a regression test that stops the same failure from coming back. it's quoted below.