Adaptive Mass-Segmented KV Compression for Long-Context Reasoning
The linear growth of the Key-Value (KV) cache is a critical bottleneck in long-form LLM inference. Existing KV compression methods mitigate this by evicting tokens based on importance scores. However, we show that their reliance on global Top-k selection triggers Region Wipe-out: the severe eviction of contiguous reasoning blocks that derails logical coherence. To address this, we propose Adaptive Mass-Segmented (AMS) KV Compression, a framework that shifts the paradigm from token-level competition to region-aware quota allocation. AMS adaptively partitions the KV cache based on the spatial distribution of attention mass, ensuring structurally vital reasoning segments receive guaranteed m...
| Authors | Junzhe Yang, Xiaoyu Shen |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 29 September 2026 |
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