More Than a Quick Glance: Overcoming the Greedy Bias in KV-Cache Compression

• URL: http://arxiv.org/abs/2602.02199v1
• Date: Mon, 02 Feb 2026 15:05:03 GMT
• Title: More Than a Quick Glance: Overcoming the Greedy Bias in KV-Cache Compression
• Authors: Aryan Sood, Tanvi Sharma, Vansh Agrawal,
• Abstract summary: LASER-KV is a framework designed to test the limits of KV compression under a strict accumulative budgeting policy.<n>Experiments on the Babilong benchmark reveal performance degradation in previous compression methods by 15-30% on various long context tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (LLMs) can theoretically support extensive context windows, their actual deployment is constrained by the linear growth of Key-Value (KV) cache memory. Prevailing compression strategies mitigate this through various pruning mechanisms, yet trade-off semantic recall for memory efficiency. In this work, we present LASER-KV (Layer Accumulated Selection with Exact-LSH Recall), a framework designed to test the limits of KV compression under a strict accumulative budgeting policy. We deviate from the standard fixed summary size approach by implementing a block-wise accumulation strategy governed by a protection divisor (n). This allows us to isolate the effects of compression from sliding window artifacts. Our experiments on the Babilong benchmark reveal performance degradation in previous compression methods by 15-30% on various long context tasks. LASER-KV maintains stable performance, achieving superior accuracies by a margin of upto 10% at 128k. These findings challenge the prevailing assumption that attention scores alone are a sufficient proxy for token utility.

Related papers

• PackKV: Reducing KV Cache Memory Footprint through LLM-Aware Lossy Compression [8.427136461713706]
  We present textbfPackKV, a generic and efficient KV cache management framework.<n>PackKV supports both latency-critical and throughput-critical inference scenarios.
  arXiv  Detail & Related papers  (2025-12-30T20:05:32Z)
• SWAN: Sparse Winnowed Attention for Reduced Inference Memory via Decompression-Free KV-Cache Compression [7.603859408568262]
  Large Language Models (LLMs) face a significant bottleneck during autoregressive inference due to the massive memory footprint of the Key-Value (KV) cache.<n>We introduce SWAN, a novel, fine-tuning-free framework that eliminates this overhead.<n>Our method uses an offline matrix to rotate and prune the KV-cache, which is then used directly in the attention computation without any reconstruction.
  arXiv  Detail & Related papers  (2025-11-24T09:41:24Z)
• The Pitfalls of KV Cache Compression [52.196873305708955]
  We show that certain instructions degrade much more rapidly with compression.<n>We show several factors that play a role in prompt leakage: compression method, instruction order, and KV eviction bias.
  arXiv  Detail & Related papers  (2025-09-30T19:55:26Z)
• 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)
• Compactor: Calibrated Query-Agnostic KV Cache Compression with Approximate Leverage Scores [37.41699761967978]
  KV cache is often the dominant resource bottleneck in real-world deployments.<n>We present Compactor, a parameter-free, query-agnostic KV compression strategy.<n>We show that Compactor achieves full KV performance on Longbench while reducing the KV memory burden by 63%, on average.
  arXiv  Detail & Related papers  (2025-07-10T20:03:35Z)
• 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)
• DBudgetKV: Dynamic Budget in KV Cache Compression for Ensuring Optimal Performance [125.81664663201282]
  We introduce a new KV cache compression method dubbed DBudgetKV.<n>It features an attention-based metric to signal when the remaining KV cache is unlikely to match the full-cache performance.<n>Our method achieves lossless KV pruning effectively and robustly, exceeding 25% compression ratio on average.
  arXiv  Detail & Related papers  (2025-02-24T06:33:39Z)
• Can LLMs Maintain Fundamental Abilities under KV Cache Compression? [29.510433427184385]
  We present a benchmark KVFundaBench to evaluate the effects of KV cache compression across diverse fundamental language models.<n>We propose ShotKV, a novel compression approach that handles prefill and decoding phases while maintaining shot-level semantic coherence.
  arXiv  Detail & Related papers  (2025-02-04T02:23:06Z)
• ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference [61.412894960600205]
  Large Language Models (LLMs) require significant GPU memory when processing long texts.<n>ChunkKV reimagines KV cache compression by treating semantic chunks as basic compression units.<n>Result: ChunkKV outperforms state-of-the-art methods by up to 8.7% in precision.
  arXiv  Detail & Related papers  (2025-02-01T03:49:47Z)
• KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing [58.29726147780976]
  We propose a plug-and-play method called textit KVSharer, which shares the KV cache between layers to achieve layer-wise compression. Experiments show that textit KVSharer can reduce KV cache computation by 30%, thereby lowering memory consumption. We verify that textit KVSharer is compatible with existing intra-layer KV cache compression methods, and combining both can further save memory.
  arXiv  Detail & Related papers  (2024-10-24T08:06:41Z)
• 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)
• KV-Compress: Paged KV-Cache Compression with Variable Compression Rates per Attention Head [0.8158530638728501]
  We introduce KV-Compress, a novel compression method that evicts contiguous KV blocks within a PagedAttention framework. Our method achieves state-of-the-art performance on LongBench for both Mistral-7B-Instruct-v0.2 and Llama-3.1-8B-Instruct while lowering the total number of compressed KVs by 4x. Evaluations on Llama-3.1-8B-Instruct and Llama-3.1-70B-Instruct-FP8 achieve compression rates up to 8x with negligible impact on performance, and up to 64x while retaining over 90% of full-cache performance
  arXiv  Detail & Related papers  (2024-09-30T19:09:13Z)

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.