[2605.26099] Language Models Need Sleep

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Computer Science > Computation and Language

arXiv:2605.26099 (cs)

[Submitted on 25 May 2026]

Title:Language Models Need Sleep

Authors:Sangyun Lee, Sean McLeish, Tom Goldstein, Giulia Fanti<br>View a PDF of the paper titled Language Models Need Sleep, by Sangyun Lee and 3 other authors

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Abstract:Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During sleep, the model performs $N$ offline recurrent passes over the accumulated context and updates the fast weights in its state-space model (SSM) blocks through a learned local rule. During inference, this shifts extra computation to sleep while preserving the latency of wake-time prediction. We test our method on controlled synthetic tasks, including cellular automata and multi-hop graph retrieval, as well as a realistic math reasoning task, on which a regular transformer as well as SSM-attention hybrid models fail. We then show that increasing sleep duration $N$ for our models improves performance, with the largest gains on examples that require deeper reasoning.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2605.26099 [cs.CL]

(or<br>arXiv:2605.26099v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.26099

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Sangyun Lee [view email]<br>[v1]<br>Mon, 25 May 2026 17:55:39 UTC (319 KB)

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