GRKV: Global Regression for Training-Free KV Cache Compression in Long-Context LLMs

• URL: http://arxiv.org/abs/2605.31105v1
• Date: Fri, 29 May 2026 10:16:30 GMT
• Title: GRKV: Global Regression for Training-Free KV Cache Compression in Long-Context LLMs
• Abstract summary: Large language models (LLMs) with extended context lengths rely on the key-value ( KV) cache to support attention over prior tokens.<n>Maintaining the KV cache incurs substantial memory overhead, motivating KV-cache compression methods.<n>We propose GRKV, a training-free KV-cache merging method that directly minimizes the discrepancy between compressed-cache and full-cache attention outputs.
• Score: 97.36238579001544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) with extended context lengths rely on the key-value (KV) cache to support attention over prior tokens. However, maintaining the KV cache incurs substantial memory overhead, motivating KV-cache compression methods that enforce a fixed budget through eviction and merging. Modern eviction methods increasingly adopt span-based retention because preserving contiguous spans is empirically effective and better preserves semantic coherence. Yet, when combined with post-eviction merging, span-based retention concentrates merges onto a small set of span-boundary carrier tokens, producing a highly imbalanced merge pattern that exacerbates over-merging and increases information loss. To address this imbalance, we propose GRKV (Global Regression for KV Cache), a training-free KV-cache merging method that directly minimizes the discrepancy between compressed-cache and full-cache attention outputs. GRKV uses ridge-regression-based merge steps to distribute information from evicted tokens across retained tokens, while regularizing the updates to prevent over-smoothing. Across the LongBench and RULER long-context benchmarks, GRKV is the only merging method that improves overall performance with minimal overhead.

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