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Modeling Language as a Sequence of Thoughts

In brief:

Transformer language models can generate strikingly natural text by modeling language as a sequence of tokens.

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New research could change how AI systems work.

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Affects widely-used AI models.

Yet, by relying primarily on surface-level co-occurrence statistics, they fail to form globally consistent latent representations of entities and events, lack of which contributes to brittleness in…

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About this source
Source
arXiv cs.CL
Type
Research Preprint
Published
Credibility
Peer-submitted research paper on arXiv

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Modeling Language as a Sequence of Thoughts

TL;DR

Transformer language models can generate strikingly natural text by modeling language as a sequence of tokens.

Quick Data

Source
https://arxiv.org/abs/2512.25026v1
Type
Research Preprint
Credibility
Peer-submitted research paper on arXiv
Published

Builder Context

Scan abstract → experiments → limitations. Also: note model size and inference requirements.

Full Analysis

Affects widely-used AI models.

Yet, by relying primarily on surface-level co-occurrence statistics, they fail to form globally consistent latent representations of entities and events, lack of which contributes to brittleness in…

Open receipts to verify and go deeper.

Source Verification

Source arXiv cs.CL
Type Research Preprint
Tier Primary Source
Assessment Peer-submitted research paper on arXiv
URL https://arxiv.org/abs/2512.25026v1
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