The Bottom Line: InfoKV combines attention scores with uncertainty signals for KV-cache compression, outperforming pure attention-based methods on long reasoning tasks by measurable margins.
Researchers introduce InfoKV, a method for compressing key-value caches in large language models that leverages uncertainty signals alongside attention weights. The approach significantly improves efficiency in long-context scenarios without sacrificing reasoning quality.
During reasoning with large language models, the key-value cache — the memory for already-processed tokens — grows substantially in both the prefilling and decoding phases. Existing compression methods rely primarily on attention weights to identify important tokens. However, this approach overlooks the fact that attention captures only local contextual patterns.
The work introduces “Forward Influence”: a metric measuring how compressed tokens impact future contexts. Analysis shows that attention-based token selection primarily influences nearby contexts. By contrast, tokens with high predictive uncertainty have significantly stronger effects on distant future contexts — an effect that pure attention methods miss.
InfoKV integrates these insights through an entropy-aware compression strategy: token-level uncertainty is combined with layer-wise representation evolution. These combined entropy scores are merged with attention scores during reasoning. Tests on long-context benchmarks using Llama-3.1, Llama-3.2, and DeepSeek-R1 show that InfoKV consistently outperforms attention-based compression methods in both scenarios — long prefilling and decoding.
For engineers, the approach is relevant because it demonstrates that information-theoretic signals (uncertainty) outperform pure structural analysis (attention) when predicting token importance. This reduces memory consumption in practical deployments without quality trade-offs.
Source: arxiv.org · Published June 24, 2026
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