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arXiv · 2609.03235

SGD-KV: Summarization Guided KV Cache Compression

Zeyu Liu·Woomin Song·Xuandi Fu·Sai Muralidhar Jayanthi·Vivek Govindan·Aram Galstyan·Sravan Babu Bodapati·Srikanth Ronanki
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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.

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Zeyu Liu, Woomin Song, Xuandi Fu, Sai Muralidhar Jayanthi, Vivek Govindan, Aram Galstyan, Sravan Babu Bodapati, Srikanth Ronanki. 2026-09-03. SGD-KV: Summarization Guided KV Cache Compression. https://arxiv.org/abs/2609.03235

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