Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility

• URL: http://arxiv.org/abs/2605.14037v1
• Date: Wed, 13 May 2026 18:58:16 GMT
• Title: Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility
• Abstract summary: We introduce Self-Pruned Key-Value Attention (SP-KV), a mechanism designed to predict future KV utility.<n>A lightweight utility predictor scores each key-value pair, and while recent KVs are always available via a local window, older pairs are written in the cache.<n>The mechanism adapts to the input and typically reduces the KV cache size by a factor of $3$ to $10times$.
• Score: 31.124813359673073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Under modern test-time compute and agentic paradigms, language models process ever-longer sequences. Efficient text generation with transformer architectures is increasingly constrained by the Key-Value cache memory footprint and bandwidth. To address this limitation, we introduce Self-Pruned Key-Value Attention (SP-KV), a mechanism designed to predict future KV utility in order to reduce the size of the long-term KV cache. This strategy operates at a fine granularity: a lightweight utility predictor scores each key-value pair, and while recent KVs are always available via a local window, older pairs are written in the cache and used in global attention only if their predicted utility surpasses a given threshold. The LLM and the utility predictor are trained jointly end-to-end exclusively through next-token prediction loss, and are adapted from pretrained LLM checkpoints. Rather than enforcing a fixed compression ratio, SP-KV performs dynamic sparsification: the mechanism adapts to the input and typically reduces the KV cache size by a factor of $3$ to $10\times$, longer sequences often being more compressible. This leads to vast improvements in memory usage and decoding speed, with little to no degradation of validation loss nor performance on a broad set of downstream tasks. Beyond serving as an effective KV-cache reduction mechanism, our method reveals structured layer- and head-specific sparsity patterns that we can use to guide the design of hybrid local-global attention architectures.

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