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LoopVQ: Sub 1-bit KV Cache Quantization for Looped Transformers

Paper recorded by Signals 4 on 2026-11-03 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.AI · 人工智能 · first seen 2026-11-04

Abstract

Looped transformers are a parameter-efficient alternative to standard transformers for language modeling. By reapplying the same layers across multiple loop steps, recent looped language models match the performance of non-looped models several times their size. While this architecture effectively reduces parameter memory, each loop step still stores its own keys and values, so the key-value (KV)

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#12 most recent of 920 cs.AI papers we have recorded · ↑ newer: You don't need long context · ↓ older: Personas Are Not People: Identity as State, Not Attribute
Cite this page: LoopVQ: Sub 1-bit KV Cache Quantization for Looped Transformers: the #12 most recent of 920 cs.AI papers we have recorded (as of 2026-11-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/loopvq-sub-1-bit-kv-cache-quantization-for-looped-transformers.html
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