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arXiv AI By Xingyu Zhu (Luke), Pu (Luke), Yi, Ziheng Cheng, Ang Lv, Jing Liu, Lexing Ying, Yiyuan Ma, Xin Dong

Periodic Weak Spots: Phase Sensitivity from Chunked KV-Cache Compression

Read the original on arXiv AI →

The paper investigates how chunked KV‑cache compression, which reduces memory and attention costs by grouping consecutive tokens, introduces a new positional coordinate called a token’s phase. It finds that models using this compression exhibit phase sensitivity: retrieval performance can vary by up to 40 percentage points depending on a token’s phase, creating periodic weak spots that average benchmark scores hide. By pretraining transformers with different compression designs and applying causal interventions, the authors show that attention components specialize asymmetrically for different phases, and theoretical analysis suggests gradient dynamics may favor such specialization.

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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