arXiv Machine Learning July 10, 2026 By Ashwin Gerard Colaco, Nada Lahjouji

What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents

Read the original on arXiv Machine Learning →

arXiv:2607. 08032v1 Announce Type: new Abstract: Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and values, carrying long prompts, maintaining recurrent state, and storing what happened in previous turns and sessions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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