# Reproducing Next-Concept Modeling (NCM): Same Quality, 31x Faster?! **URL:** \ **Category:** Content Creators\ **Tags:** onchain-ai-garage, ncm, machine-learning, benchmarking\ **Created:** [10 November 2026 15:00 UTC](https://www.artofsm.art/t/reproducing-next-concept-modeling-ncm-same-quality-31x-faster/28055 "2026-11-10T15:00:23Z")\ **Posts on this page:** 1\ **Showing post:** 3
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [10 November 2026 15:25 UTC](https://www.artofsm.art/t/reproducing-next-concept-modeling-ncm-same-quality-31x-faster/28055/3 "2026-11-10T15:25:04Z")
The video explores Next Concept Modeling (NCM), a novel AI architecture that differs from traditional large language models (LLMs) and diffusion models in how it generates outputs. Unlike standard LLMs that generate text token-by-token in a left-to-right sequence (autoregressive generation), or diffusion models that iteratively refine noisy data, NCM builds layered internal representations from coarse to fine. These layers contain learned codes that are not human-readable but allow the model to generate outputs more efficiently by predicting entire layers in parallel, rather than sequentially token-by-token. The training process for NCM involves two main stages. First, the model learns to encode completed answers into layered internal codes and then decode them back to reconstruct the original answer, effectively learning a compact representation. Second, a generator model is trained to predict these layered codes from incomplete inputs, such as unsolved Sudoku puzzles. This approach allows the model to generate solutions by progressively refining its internal representation through a series of layers, each building on the previous one, which reduces the number of sequential steps required compared to traditional models. The presenter conducted a mini experiment by training a small NCM model on Sudoku puzzles using a DGX Spark system. The model was trained on 20,000 puzzles with 12 layers per answer. Initial results showed moderate success, with about 39% accuracy in solving puzzles. Improvements were made by increasing the training data and introducing a technique called conditioning corruption, where the model practiced recovering from imperfect intermediate representations. This boosted the success rate to 58%, demonstrating the model’s ability to handle errors and improve solution accuracy. For comparison, the presenter also trained two other models on the same Sudoku dataset: a traditional autoregressive model and a diffusion model. The autoregressive model performed poorly with only 9.5% accuracy, while the diffusion model achieved up to 60.7% accuracy but required significantly more inference time. In contrast, the NCM model reached 58.1% accuracy with a median inference time of just 58.7 milliseconds, making it about eight times faster than the diffusion model at similar accuracy levels. This highlights NCM’s potential for faster reasoning tasks without a substantial loss in performance. In conclusion, the video showcases NCM as a promising alternative AI architecture that balances speed and accuracy by leveraging a coarse-to-fine layered representation approach. While the mini experiment used smaller models and datasets, the results align with the original research claims of faster generation speeds for tasks like Sudoku solving, summarization, and machine translation. The presenter encourages further exploration of NCM and similar architectures as they offer interesting new directions beyond traditional LLMs and diffusion models in AI development. ## Useful Links - [Next Concept Modeling (NCM) paper on arXiv](https://arxiv.org/abs/2610.14162) — Central to understanding the NCM architecture and its claims about speed and quality. --- _[View the full topic](https://www.artofsm.art/t/reproducing-next-concept-modeling-ncm-same-quality-31x-faster/28055)._