The Intern-NCP Team claims its 8.9B NCP-ArchPreview matches OLMo-3-7B's final pretraining loss using 51.3% of its training tokens and approaches a parameter-matched baseline using 85% of standard computation, potentially reducing training costs through next-concept prediction.

state: seedheat: mediumuncertainty: mediumnovelscott: lowlatent-language-models model-architecture training-efficiencyIntern-NCP Team

What is this?

NCP-ArchPreview is an 8.9B-parameter latent-space language model described in a technical report by the Intern-NCP Team, listed with Shanghai AI Lab, LUMIA Lab and SJTU affiliations. The supplied snippets describe training on 5.73T Dolma-3 tokens, jointly predicting next tokens and learned discrete concepts, then feeding predicted concepts back into token generation. The team reports reaching OLMo-3-7B’s final pretraining loss with 51.3% of its training tokens and, in a separate parameter-matched comparison, approaching an 8.9B baseline’s loss using 85% of standard computation. These are reported experimental results, not independently verified savings: the snippets do not establish wall-clock or monetary training costs, and token efficiency should not be equated with compute efficiency.

Why it matters to Scott

No meaningful intersection found with Scott’s supplied positions or active projects: these pretraining-efficiency claims establish neither cheaper inference for his systems nor a challenge to his external, legible wiki-learning architecture. The radar already tracks training efficiency as a concept, but the supplied hits do not show it tracking this development.

radar:concept.training-efficiency

queries asked of Scott's wikis

latent representations concept learning versus token prediction
training efficiency compute economics parameter-matched baselines
model architecture changes versus scaling training data
lightweight domain adaptation learned codebooks
speculative decoding drafter acceptance inference efficiency

Measured heat

now 0 pts/hpeak 0 pts/hcomments 0/hpeers p14momentum: steady2 platformsage 794h

points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

09-08 14:00	⭐ origin echo-reconstructed	Introduces joint next-token and next-concept prediction with standard autoregressive generation, reporting reduced training-token and comput Intern-NCP Team on paper (echo) · attributed from hn.story.49700264	—
09-14 17:02	first on hacker news · published · +147.1h	NCP-ArchPreview: 8.9B latent LM matches OLMo-3-7B on 51% of tokens iamsyr	—
09-14 17:02	amplified on hacker news 👑	hn.story.49700264 iamsyr	peak 2 · 0 comments · 98% of case engagement
09-14 17:22	our radar first saw it · +147.4h	discovery anchor: hn.story.49700264	—

pace: p28 vs 519 stories at the 720h mark (now 794h old) — ahead of aafp-commons-signed-agent-notebook (2.0x), behind agentgate-signed-agent-receipts (0.7x)

Evidence (2) — ⭐ canonical anchor

source	object	author	score	comments
🟧 hn	NCP-ArchPreview: 8.9B latent LM matches OLMo-3-7B on 51% of tokens Retrieved article excerpt Open article · Retrieved 2026-09-14T17:24:44.434609+00:00 # Computer Science > Computation and Language arXiv:2609.10715 (cs) [Submitted on 9 Sep 2026] # Title:NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction Authors:The [Intern-NCP Team](https://arxiv.org/search/cs?searchtype=author&query=Intern-NCP+Team): [Jiaqi Cao](https://arxiv.org/search/cs?searchtype=author&query=Cao,+J), [Chiyu Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+C), [Shuang Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+S), [Xu Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+X), [Beiya Dai](https://arxiv.org/search/cs?searchtype=author&query=Dai,+B), [Yufan Feng](https://arxiv.org/search/cs?searchtype=author&query=Feng,+Y), [Kewen Ge](https://arxiv.org/search/cs?searchtype=author&query=Ge,+K), [Ruijun Ge](https://arxiv.org/search/cs?searchtype=author&query=Ge,+R), [Jiayi Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+J), [Yang Jiao](https://arxiv.org/search/cs?searchtype=author&query=Jiao,+Y), [Dahua Lin](https://arxiv.org/search/cs?searchtype=author&query=Lin,+D), [Zhouhan Lin](https://arxiv.org/search/cs?searchtype=author&query=Lin,+Z), [Yifan Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y), [Yuliang Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y), [Biqing Qi](https://arxiv.org/search/cs?searchtype=author&query=Qi,+B), [Mowen Ruan](https://arxiv.org/search/cs?searchtype=author&query=Ruan,+M), [Junzhe Shen](https://arxiv.org/search/cs?searchtype=author&query=Shen,+J), [Yunchong Song](https://arxiv.org/search/cs?searchtype=author&query=Song,+Y), [Hao Sun](https://arxiv.org/search/cs?searchtype=author&query=Sun,+H), [Zhongbo Tian](https://arxiv.org/search/cs?searchtype=author&query=Tian,+Z), [Yixuan Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y), [Rubin Wei](https://arxiv.org/search/cs?searchtype=author&query=Wei,+R), [Jiaxin Xiong](https://arxiv.org/search/cs?searchtype=author&query=Xiong,+J), [Kangyu Yang](https://arxiv.org/search/cs?searchtype=author&query=Yang,+K), [Qian Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+Q), [Qi Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Q), [Bowen Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+B) 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](https://arxiv.org/pdf/2609.10715) > 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](https://arxiv.org/abs/2609.10715) [cs.CL] \| \| \| (or [arXiv:2609.10715v1](https://arxiv.org/abs/2609.10715v1) [cs.CL] for this version) \| \| \| <https://doi.org/10.48550/arXiv.2609.10715> Focus to learn more arXiv-issued DOI via DataCite (pending registration) \| ## Submission history From: Yuliang Liu [[view email](https://arxiv.org/show-email/46186b75/2609.10715)] [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](https://arxiv.org/pdf/2609.10715) - [TeX Source](https://arxiv.org/src/2609.10715) [view license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/ "Rights to this article") ### Current browse context: cs.CL [< prev](https://arxiv.org/prevnext?id=2609.10715&function=prev&context=cs.CL "previous in cs.CL (accesskey p)") \| [next >](https://arxiv.org/prevnext?id=2609.10715&function=next&context=cs.CL "next in cs.CL (accesskey n)") [new](https://arxiv.org/list/cs.CL/new) \| [recent](https://arxiv.org/list/cs.CL/recent) \| [2026-09](https://arxiv.org/list/cs.CL/2026-09) Change to browse by: [cs](https://arxiv.org/abs/2609.10715?context=cs) ### References & Citations - [NASA ADS](https://ui.adsabs.harvard.edu/abs/arXiv:2609.10715) - [Google Scholar](https://scholar.google.com/scholar_lookup?arxiv_id=2609.10715) - [Semantic Scholar](https://api.semanticscholar.org/arXiv:2609.10715) export BibTeX citation Loading... ## BibTeX formatted citation × loading... 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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](https://info.arxiv.org/labs/index.html). [Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2609.10715) \| Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))	iamsyr	2	0
🟧 echo.paper ⭐	Introduces joint next-token and next-concept prediction with standard autoregressive generation, reporting reduced training-token and comput	Intern-NCP Team	—	—

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🟧 hn

NCP-ArchPreview: 8.9B latent LM matches OLMo-3-7B on 51% of tokens

Retrieved article excerpt

Open article · Retrieved 2026-09-14T17:24:44.434609+00:00

# Computer Science > Computation and Language

**arXiv:2609.10715** (cs)

[Submitted on 9 Sep 2026]

# Title:NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

Authors:The [Intern-NCP Team](https://arxiv.org/search/cs?searchtype=author&query=Intern-NCP+Team): [Jiaqi Cao](https://arxiv.org/search/cs?searchtype=author&query=Cao,+J), [Chiyu Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+C), [Shuang Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+S), [Xu Cheng](https://arxiv.org/search/cs?searchtype=author&query=Cheng,+X), [Beiya Dai](https://arxiv.org/search/cs?searchtype=author&query=Dai,+B), [Yufan Feng](https://arxiv.org/search/cs?searchtype=author&query=Feng,+Y), [Kewen Ge](https://arxiv.org/search/cs?searchtype=author&query=Ge,+K), [Ruijun Ge](https://arxiv.org/search/cs?searchtype=author&query=Ge,+R), [Jiayi Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+J), [Yang Jiao](https://arxiv.org/search/cs?searchtype=author&query=Jiao,+Y), [Dahua Lin](https://arxiv.org/search/cs?searchtype=author&query=Lin,+D), [Zhouhan Lin](https://arxiv.org/search/cs?searchtype=author&query=Lin,+Z), [Yifan Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y), [Yuliang Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y), [Biqing Qi](https://arxiv.org/search/cs?searchtype=author&query=Qi,+B), [Mowen Ruan](https://arxiv.org/search/cs?searchtype=author&query=Ruan,+M), [Junzhe Shen](https://arxiv.org/search/cs?searchtype=author&query=Shen,+J), [Yunchong Song](https://arxiv.org/search/cs?searchtype=author&query=Song,+Y), [Hao Sun](https://arxiv.org/search/cs?searchtype=author&query=Sun,+H), [Zhongbo Tian](https://arxiv.org/search/cs?searchtype=author&query=Tian,+Z), [Yixuan Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y), [Rubin Wei](https://arxiv.org/search/cs?searchtype=author&query=Wei,+R), [Jiaxin Xiong](https://arxiv.org/search/cs?searchtype=author&query=Xiong,+J), [Kangyu Yang](https://arxiv.org/search/cs?searchtype=author&query=Yang,+K), [Qian Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+Q), [Qi Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Q), [Bowen Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+B)

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](https://arxiv.org/pdf/2609.10715)
> 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](https://arxiv.org/abs/2609.10715) [cs.CL] |
|  | (or  [arXiv:2609.10715v1](https://arxiv.org/abs/2609.10715v1) [cs.CL] for this version) |
|  | <https://doi.org/10.48550/arXiv.2609.10715> Focus to learn more  arXiv-issued DOI via DataCite (pending registration) |

## Submission history

From: Yuliang Liu [[view email](https://arxiv.org/show-email/46186b75/2609.10715)]   
 **[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](https://arxiv.org/pdf/2609.10715)
- [TeX Source](https://arxiv.org/src/2609.10715)

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iamsyr

🟧 echo.paper ⭐

Introduces joint next-token and next-concept prediction with standard autoregressive generation, reporting reduced training-token and comput

Intern-NCP Team

—

Interpretation history

2026-09-14T17:44:57Z
grounded: novel/low — No meaningful intersection found with Scott’s supplied positions or active projects: these pretraining-efficiency claims establish neither cheaper inference for
2026-09-14T17:40:33Z
case created — The technical report makes concrete architecture and training-efficiency claims, with a parameter-aligned comparison that qualifies the headline.

Decision trace

10-05 04:43review_dormantscheduled targets exhausted or 28 quiet days
10-05 04:43drop_targetsquiet through full ladder or over cap 8
09-15 03:44groundNo meaningful intersection found with Scott’s supplied positions or active projects: these pretraining-efficiency claims establish neither cheaper inference for his systems nor a challenge to his exte
09-15 03:40createThe technical report makes concrete architecture and training-efficiency claims, with a parameter-aligned comparison that qualifies the headline.