Information-Aware KV Cache Compression for Long Reasoning
Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling and decoding stages. Existing KV cache compression methods mainly rely on attention weights to estimate token importance. While attention effectively captures contextual relevance, it overlooks complementary information-theoretic signals related to predictive uncertainty and token informativeness. In this paper, we revisit token importance from a forward-looking perspective and introduce \textit{Forward Influence}, a metric that measures how compressed tokens affect future contexts. Our analysis reveals that tokens selected by attention scores...
| Authors | Jushi Kai, Zhuiri Xiao, Alexandra Birch et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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