AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[Submitted on 9 Sep 2026] Title:NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction View a PDF of the paper titled NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction, by The Intern-NCP Team: Jiaqi Cao and Chiyu Chen and Shuang Cheng and Xu Cheng and Beiya Dai and Yufan Feng and Kewen Ge and Ruijun Ge and Jiayi Huang and Yang Jiao and Dahua Lin and Zhouhan Lin and Yifan Liu and Yuliang Liu and Biqing Qi and Mowen Ruan and Junzhe Shen and Yunchong Song and Hao Sun and Zhongbo Tian and Yixuan Wang and Rubin Wei and Jiaxin Xiong and Kangyu Yang and Qian Yao and Qi Zhang and Bowen Zhou View PDF Abstract:We introduce NCP-ArchPreview, a latent-space language model that pushes autoregressive pretraining beyond standard next-token prediction (NTP). Alongside NTP, the model learns through Next Concept Prediction (NCP) to predict discrete concepts that span multiple tokens, introducing an explicit and more challenging concept-level objective while preserving standard token-level autoregressive generation. NCP-ArchPreview builds a latent space by constructing a product-quantized concept vocabulary directly from its hidden states, and subsequently learns to predict future concepts via a dedicated Concept Module. These predicted concepts are then fed back to the token level to guide subsequent generation, with NTP and NCP trained jointly end-to-end. We scale this architecture to 8.9B parameters and train it on 5.73T tokens from the Dolma-3 dataset, marking the largest demonstration of a latent-space language model to date. Remarkably, by consuming only 51.3% of the total training tokens, NCP-ArchPreview achieves the final pretraining loss of OLMo-3-7B. Following full pretraining, it outperforms OLMo-3-7B by 2.45 points on the downstream macro-average, including a notable 5.99-point gain on GSM8K. Controlled experiments isolate a clear progression of performance gains stemming from both the latent architecture and the NCP objective. Furthermore, utilizing only 85% of the standard computation, NCP-ArchPreview approaches the training loss of a strictly parameter-aligned 8.9B baseline. The learned latent space remains highly valuable after the pretraining stage: updating just the 17M-parameter VQ module yields a novel, lightweight interface for domain adaptation, while a simple injection of concept representations into a DFlash2 drafter improves the mean accepted length by 4.17% with negligible overhead. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.10715 [cs.CL] (or arXiv:2609.10715v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.10715 arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuliang Liu [view email] [v1] Wed, 9 Sep 2026 18:12:43 UTC (2,701 KB) Full-text links: Access Paper: View a PDF of the paper titled NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction, by The Intern-NCP Team: Jiaqi Cao and Chiyu Chen and Shuang Cheng and Xu Cheng and Beiya Dai and Yufan Feng and Kewen Ge and Ruijun Ge and Jiayi Huang and Yang Jiao and Dahua Lin and Zhouhan Lin and Yifan Liu and Yuliang Liu and Biqing Qi and Mowen Ruan and Junzhe Shen and Yunchong Song and Hao Sun and Zhongbo Tian and Yixuan Wang and Rubin Wei and Jiaxin Xiong and Kangyu Yang and Qian Yao and Qi Zhang and Bowen Zhou View PDF TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)