SGD-KV: Summarization Guided KV Cache Compression

Abstract:Large language models (LLMs) face severe memory bottlenecks in long-context inference due to the linearly growing size of key-value (KV) caches. Existing KV cache compression techniques typically rely on simple heuristics, overlooking the distinct functional roles of different attention heads. We present SGD-KV (Summarization-Guided KV Cache Compression), a head-aware framework that leverages a novel chunk-summarization diagnostic task to systematically identify and prioritize attention heads specialized in hierarchical information aggregation. Experiments on Qwen2.5-7B-1M and Qwen3-32B across diverse long-context benchmarks demonstrate that SGD-KV achieves state-of-the-art performance with contexts up to 1M tokens, while reducing KV cache memory usage by up to 75%. Our findings show that strategically allocating the KV cache budget based on the summarization score distribution of attention heads yields a superior efficiency-accuracy trade-off for long-context inference.

Computation and Language (cs.CL)

arXiv:2609.03235 [cs.CL]

(or arXiv:2609.03235v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.03235 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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