EchoKV introduces a reversible KV cache compression scheme that allows LLMs to switch back to full-precision inference on-demand.
March 25, 2026
Original Paper
EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction
arXiv · 2603.22910
The Takeaway
Unlike standard compression methods that permanently lose information, EchoKV uses lightweight reconstruction to transition between standard and compressed states. This flexibility is critical for production environments where memory availability fluctuates across different request workloads.
From the abstract
The increasing memory demand of the Key-Value (KV) cache poses a significant bottleneck for Large Language Models (LLMs) in long-context applications. Existing low-rank compression methods often rely on irreversible parameter transformations, sacrificing the flexibility to switch back to full-precision inference when memory is abundant. In this paper, we propose EchoKV, a flexible KV cache compression scheme that enables on-demand transitions between standard and compressed inference. Unlike tra