用於長推理的資訊感知型KV快取壓縮

摘要

大语言模型（LLM）的推理能力已取得显著进展，这导致其在预填充（prefilling）和解码（decoding）阶段的键值缓存（KV cache）规模持续增长。现有KV缓存压缩方法主要依赖注意力权重评估Token重要性。尽管注意力机制能有效捕捉上下文相关性，却忽略了与预测不确定性和Token信息量相关的互补性信息论信号。本文从前瞻性视角重新审视Token重要性，提出"前向影响力"（Forward Influence）这一度量指标，用以衡量压缩Token对未来上下文的影响程度。分析表明，注意力分数选取的Token主要影响邻近上下文，而与高预测不确定性相关的Token对远距离未来上下文具有显著更强的影响力。基于这一发现，我们提出信息熵感知的KV缓存压缩框架InfoKV，该框架融合信息论信号，将Token级预测不确定性与层级表征演化相结合，并在推理过程中将生成的熵分数与注意力分数进行集成。在Llama-3.1、Llama-3.2和DeepSeek-R1上的长上下文推理基准测试中，InfoKV在长预填充和解码场景下均持续优于现有基于注意力的KV压缩方法。

English

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 Forward Influence, a metric that measures how compressed tokens affect future contexts. Our analysis reveals that tokens selected by attention scores mainly influence nearby contexts, whereas tokens associated with high predictive uncertainty exhibit substantially stronger influence on distant future contexts. Based on the observation, we propose InfoKV, an entropy-aware KV cache compression framework that incorporates information-theoretic signals. It combines token-level predictive uncertainty with layer-wise representation evolution and integrates the resulting entropy scores with attention scores during reasoning. Experiments on long-context reasoning benchmarks with Llama-3.1, Llama-3.2, and DeepSeek-R1 demonstrate that InfoKV consistently outperforms existing attention-based KV compression methods in both long prefilling and decoding scenarios.