Reformulating KV Cache Eviction Problem for Long-Context LLM Inference

Researchers introduce LaProx, a novel KV Cache eviction strategy for long-context LLM inference that reformulates the problem from head-wise weight averaging to output-aware layer-wise matrix multiplication. The method achieves 2× accuracy loss reduction under extreme compression while maintaining performance with just 5% of the original KV cache.

The paper addresses a critical bottleneck in deploying large language models at scale: the exponential memory consumption of Key-Value caches during long-context inference. As LLMs process longer sequences, KV cache growth becomes prohibitively expensive, limiting practical applications in document analysis, code understanding, and extended reasoning tasks. LaProx reformulates how tokens are evaluated for eviction by moving beyond simple attention weight metrics to account for value representations and inter-head dependencies, creating a more holistic importance scoring system.

This advancement builds on ongoing efforts to optimize transformer inference efficiency. Previous approaches treated KV cache pruning as a local, head-specific problem, missing crucial multiplicative interactions between attention patterns and value states across the model. By proposing the first globally comparable importance scores across all layers, LaProx enables unified token selection rather than independent head-level decisions, fundamentally improving the approximation quality.

The practical implications are substantial for AI infrastructure and deployment. Reducing KV cache requirements to 5% of original size dramatically decreases memory bandwidth, latency, and computational overhead—directly enabling longer context windows on consumer hardware and reducing data center costs for inference services. Maintaining accuracy under such extreme compression represents a significant technical achievement that could accelerate adoption of long-context capabilities in production systems.

The comprehensive evaluation across 19 datasets and two major benchmarks (LongBench and Needle-In-A-Haystack) demonstrates broad applicability. As inference efficiency becomes increasingly competitive, techniques that preserve model capability while reducing computational demands will influence both open-source and commercial LLM deployment strategies moving forward.

• →LaProx reduces KV cache memory requirements to 5% while maintaining model performance through output-aware token importance scoring
• →The method achieves up to 2× accuracy loss reduction compared to existing approaches under extreme compression scenarios
• →Global token importance scores enable model-wide selection instead of local head-wise eviction decisions
• →Novel approach accounts for multiplicative interactions between attention maps and projected value states rather than relying solely on attention weights
• →Comprehensive testing across 19 datasets demonstrates consistent performance improvements over prior KV cache optimization methods