HeatKV: Head-tuned KV-cache Compression for Visual Autoregressive Modeling

• URL: http://arxiv.org/abs/2605.14877v1
• Date: Thu, 14 May 2026 14:22:34 GMT
• Title: HeatKV: Head-tuned KV-cache Compression for Visual Autoregressive Modeling
• Abstract summary: HeatKV is a novel compression method that adapts cache allocation in each head based on its attention to previously generated scales.<n>HeatKV achieves a new state-of-the-art (SOTA) for VAR model KV-cache compression, showcasing the effectiveness of fine-grained, head-specific cache allocation.
• Score: 2.8560048042907744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Autoregressive (VAR) models have recently demonstrated impressive image generation quality while maintaining low latency. However, they suffer from severe KV-cache memory constraints, often requiring gigabytes of memory per generated image. We introduce HeatKV, a novel compression method that adapts cache allocation in each head based on its attention to previously generated scales. Using a small offline calibration set, the attention heads are ranked according to their attention scores over prior scales. Based on this ranking, we construct a static pruning schedule tailored to a given memory budget. Applied to the Infinity-2B model, HeatKV achieves $2 \times$ higher compression ratio in memory allocation for KV cache compared to existing methods, while maintaining similar or better image fidelity, prompt alignment and human perception score. Our method achieves a new state-of-the-art (SOTA) for VAR model KV-cache compression, showcasing the effectiveness of fine-grained, head-specific cache allocation.

Related papers

• 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)
• 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)
• R-KV: Redundancy-aware KV Cache Compression for Reasoning Models [77.84539432982307]
  We propose Redundancy-aware KV Cache Compression for Reasoning models (R-KV)<n>R-KV preserves nearly 100% of the full KV cache performance using only 10% of the KV cache.<n>Remarkably, R-KV even achieves 105% of full KV cache performance with 16% of the KV cache.
  arXiv  Detail & Related papers  (2025-05-30T02:03:24Z)
• Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV Cache Compression [21.840636839249026]
  We introduce ScaleKV, a novel KV cache compression framework tailored for Visual Autoregressive ( VAR) architectures.<n>Based on two critical observations, ScaleKV categorizes transformer layers into two functional groups: drafters and refiners.<n>Our approach effectively reduces the required KV cache memory to 10% while preserving pixel-level fidelity.
  arXiv  Detail & Related papers  (2025-05-26T07:11:42Z)
• 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)
• BaKlaVa -- Budgeted Allocation of KV cache for Long-context Inference [6.222836318380985]
  BaKlaVa is a method to allocate optimal memory for individual KV-caches across the model.<n>We evaluate our method on LLaMA-3-8B, and Qwen2.5-7B models.
  arXiv  Detail & Related papers  (2025-02-18T04:08:29Z)
• 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)
• Lossless KV Cache Compression to 2% [22.98828332096935]
  This work introduces a novel architecture, Cross-Layer Latent Attention (CLLA), aimed at compressing the KV cache to less than 2% of its original size. CLLA integrates attention head/dimension reduction, layer sharing, and quantization techniques, into a cohesive framework.
  arXiv  Detail & Related papers  (2024-10-20T02:17:35Z)
• ThinK: Thinner Key Cache by Query-Driven Pruning [63.13363917871414]
  Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications.<n>This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference.<n>We propose ThinK, a novel query-dependent KV cache pruning method designed to minimize attention weight loss while selectively pruning the least significant channels.
  arXiv  Detail & Related papers  (2024-07-30T17:59:08Z)
• PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling [38.732413451399]
  Pyramid KV is a novel and effective KV cache compression method.<n>We show that Pyramid KV matches the performance of models with a full KV cache while retaining only 12% of the KV cache.<n>In the Needle-in-a-Haystack experiment, Pyramid KV outperforms competing methods in maintaining long-context comprehension.
  arXiv  Detail & Related papers  (2024-06-04T07:51:30Z)
• Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs [82.08922896531618]
  We introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs) We conduct targeted profiling to discern the intrinsic structure of attention modules. Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens.
  arXiv  Detail & Related papers  (2023-10-03T05:17:08Z)

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.