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arXiv AI By Mengming Li, Ceyu XU, Qijun Zhang, Jiangnan Yu, Xiangfeng Sun, Haohui Mai, Zhiyao Xie

Memory Compression for High-Fanout Agent Sandboxes

Read the original on arXiv AI →

The paper introduces AgentZip, a memory compression system tailored for high‑fanout AI‑agent sandboxes. It exploits template‑relative and cross‑sandbox redundancy, expands compression to any profitable page, and shifts overhead control to restore‑time prefetching. The approach aligns compression with LLM waiting periods, achieving up to 8.7× memory reduction and reducing slowdown to 1.40× while preserving most savings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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