ProxyKV: Cross-Model Proxy Pruning for Efficient Long-Context LLM Inference

📰 ArXiv cs.AI

Learn how ProxyKV enables efficient long-context inference in Large Language Models by bridging the scoring-cost-accuracy gap with cross-model proxy pruning

advanced Published 19 May 2026

Action Steps

Implement ProxyKV framework to offload importance scoring
Apply cross-model proxy pruning to reduce KV cache memory wall
Evaluate the trade-off between scoring cost and accuracy in LLM inference
Compare ProxyKV with existing pruning methods for precision and latency
Integrate ProxyKV with existing LLM architectures for efficient long-context inference

Who Needs to Know This

ML engineers and researchers working on LLMs can benefit from this technique to improve inference efficiency without sacrificing precision

Key Insight

💡 ProxyKV bridges the scoring-cost-accuracy gap in LLM inference by offloading importance scoring with cross-model proxy pruning

Full Article

Title: ProxyKV: Cross-Model Proxy Pruning for Efficient Long-Context LLM Inference

Abstract:
arXiv:2605.16360v1 Announce Type: cross Abstract: Efficient long-context inference in Large Language Models (LLMs) is severely constrained by the Key-Value (KV) cache memory wall, yet existing pruning methods force a choice between low-latency heuristics that sacrifice precision and high-precision reconstruction methods that incur prohibitive prefilling overhead. To bridge this scoring-cost--accuracy gap, we propose ProxyKV, a cross-model proxy pruning framework that offloads importance scoring

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