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Computer Science > Computation and Language

arXiv:2510.27688 (cs)
[Submitted on 31 Oct 2025]

Title:Continuous Autoregressive Language Models

Authors:Chenze Shao, Darren Li, Fandong Meng, Jie Zhou
View a PDF of the paper titled Continuous Autoregressive Language
Models, by Chenze Shao and 3 other authors
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    Abstract:The efficiency of large language models (LLMs) is
    fundamentally limited by their sequential, token-by-token
    generation process. We argue that overcoming this bottleneck
    requires a new design axis for LLM scaling: increasing the
    semantic bandwidth of each generative step. To this end, we
    introduce Continuous Autoregressive Language Models (CALM), a
    paradigm shift from discrete next-token prediction to continuous
    next-vector prediction. CALM uses a high-fidelity autoencoder to
    compress a chunk of K tokens into a single continuous vector,
    from which the original tokens can be reconstructed with over
    99.9\% accuracy. This allows us to model language as a sequence
    of continuous vectors instead of discrete tokens, which reduces
    the number of generative steps by a factor of K. The paradigm
    shift necessitates a new modeling toolkit; therefore, we develop
    a comprehensive likelihood-free framework that enables robust
    training, evaluation, and controllable sampling in the continuous
    domain. Experiments show that CALM significantly improves the
    performance-compute trade-off, achieving the performance of
    strong discrete baselines at a significantly lower computational
    cost. More importantly, these findings establish next-vector
    prediction as a powerful and scalable pathway towards
    ultra-efficient language models. Code: this https URL. Project:
    this https URL.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2510.27688 [cs.CL]
          (or arXiv:2510.27688v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2510.27688
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Chenze Shao [view email]
[v1] Fri, 31 Oct 2025 17:58:11 UTC (133 KB)
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