AI RESEARCH
CASK: Core-Aware Selective KV Compression for Reasoning Traces
arXiv CS.AI
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ArXi:2604.10900v1 Announce Type: new In large language models performing long-form reasoning, the KV cache grows rapidly with decode length, creating bottlenecks in memory and inference stability. Existing reasoning-oriented KV compression has mostly followed an eviction-centered view: estimate token importance accurately, then discard lower-ranked entries. Our analysis suggests that scorer refinement alone often fails to substantially reorganize the actual keep-set and may therefore not be the main lever for preserving reasoning behavior.