EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction

• URL: http://arxiv.org/abs/2603.22910v1
• Date: Tue, 24 Mar 2026 07:58:42 GMT
• Title: EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction
• Abstract summary: EchoKV is a flexible KV cache compression scheme that enables on-demand transitions between standard and compressed inference.<n>We introduce a two-stage fine-tuning strategy that allows for rapid, low-cost training.
• Score: 55.026048429595384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 traditional compression-decompression paradigms, EchoKV utilizes a lightweight network to reconstruct the residual KV components from a partial subset, leveraging intrinsic inter-layer and intra-layer similarities among attention heads. We further introduce a two-stage fine-tuning strategy that allows for rapid, low-cost training (e.g., ~1 A100 GPU-hour for a 7B model). Experimental results on LongBench and RULER demonstrate that EchoKV consistently outperforms existing methods across various compression ratios while maintaining high throughput for short-context scenarios.

Related papers

• End-to-End Context Compression at Scale [81.70601323130997]
  Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length.<n>Recent techniques to compress the KV cache fall short: they either degrade model quality substantially or require considerable time and compute to compress a single long prompt.<n>Existing approaches are not competitive with KV cache compression on the accuracy-efficiency frontier.
  arXiv  Detail & Related papers  (2026-06-08T15:43:16Z)
• DeltaKV: Residual-Based KV Cache Compression via Long-Range Similarity [50.52392445266824]
  We propose a residual-based KV cache compression framework motivated by long-range inter-token similarity and highly shared latent components in KV representations.<n>Instead of discarding tokens, DeltaKV encodes semantic residuals relative to retrieved historical references, preserving fidelity while substantially reducing storage.<n>Experiments show that DeltaKV reduces KV cache memory to 29% of the original while maintaining near-lossless accuracy on LongBench, SCBench, and AIME.
  arXiv  Detail & Related papers  (2026-02-08T15:14:36Z)
• One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache [38.49582847975703]
  We propose DynaKV, a novel post-training framework for low-rank KV cache compression.<n>Our method consistently outperforms existing state-of-the-art compression techniques.<n>When integrated with SnapKV, DynaKV retains only 6% of the KV cache while maintaining 94% of the baseline performance on the LongBench benchmark.
  arXiv  Detail & Related papers  (2026-02-03T13:20:36Z)
• CommonKV: Compressing KV Cache with Cross-layer Parameter Sharing [54.34080239841088]
  CommonKV is a training-free method for cross-layer KV cache compression through adjacent parameters sharing.<n>We show that the proposed method consistently outperforms existing low-rank and cross-layer approaches at various compression ratios.
  arXiv  Detail & Related papers  (2025-08-22T06:55:45Z)
• FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression [18.12657364501536]
  FAEDKV is a novel, training-free KV cache compression framework.<n>It preserves both early and recent contextual information.<n>Experiments on LongBench benchmark demonstrate FAEDKV's superiority over existing methods by up to 22%.
  arXiv  Detail & Related papers  (2025-07-26T18:20:25Z)
• ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration [69.57122277845293]
  We propose ReCalKV, a post-training low-rank KV cache compression approach with tailored strategies for Keys and Values.<n>For Keys, we propose Similarity aware Recontext (HSR), which clusters structurally similar heads into groups, enabling more accurate low-rank approximation.<n>For Values, we propose Offline Head-wise Value (OVC), which efficiently calibrates the value projection matrix using calibration data without training.
  arXiv  Detail & Related papers  (2025-05-30T08:49:27Z)
• FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management [48.904743679691414]
  FlowKV is a novel multi-turn isolation mechanism for KV Cache management.<n>It preserves the accumulated compressed KV cache from past turns.<n>It prevents the re-compression of older context and thereby mitigating catastrophic forgetting.
  arXiv  Detail & Related papers  (2025-05-21T10:20:46Z)
• LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy [59.1298692559785]
  Key-Value ( KV) cache is crucial component in serving transformer-based autoregressive large language models (LLMs) Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages; (2) KV cache compression at test time; and (3) KV cache compression at test time. We propose a low-rank approximation of KV weight matrices, allowing plug-in integration with existing transformer-based LLMs without model retraining. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages.
  arXiv  Detail & Related papers  (2024-10-04T03:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.