CompactAttention: Accelerating Chunked Prefill with Block-Union KV Selection

• URL: http://arxiv.org/abs/2605.16839v1
• Date: Sat, 16 May 2026 06:47:41 GMT
• Title: CompactAttention: Accelerating Chunked Prefill with Block-Union KV Selection
• Abstract summary: Existing sparse attention methods do not translate efficiently to chunked prefill.<n>We propose CompactAttention, a chunked-prefill attention mechanism based on Block-Union KV Selection.<n>It delivers up to 2.72$times$ attention speedup at 128K context length under chunked prefill.
• Score: 13.937483869660648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chunked prefill has become a widely adopted serving strategy for long-context large language models, but efficient attention computation in this regime remains challenging. Existing sparse attention methods are primarily designed for one-shot prefill and do not translate efficiently to chunked prefill: block-sparse kernels lose efficiency when the query length is limited by the chunk size, while fine-grained pattern search becomes costly when repeated over the accumulated KV cache at every chunk. QUOKA, a recent method that directly targets chunked prefill, avoids sparse-kernel overhead but relies on query-subsampled, token-level KV selection, which can miss query-specific KV entries and introduce explicit KV-copy overhead. To address these limitations, we propose CompactAttention, a chunked-prefill attention mechanism based on Block-Union KV Selection. CompactAttention treats 2D block-sparse masks as KV-selection signals rather than direct sparse-kernel execution plans, and converts them into GQA-aware per-group KV block tables through Q-block union and intra-group union. This construction produces the minimal block tables that preserve all KV blocks selected by the input masks under paged execution constraints, enabling selected KV blocks to be accessed in place without explicit KV compaction. On LLaMA-3.1-8B-Instruct, CompactAttention maintains accuracy close to dense attention on the RULER benchmark while delivering up to 2.72$\times$ attention speedup at 128K context length under chunked prefill.

Related papers

• CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration [67.17709877327232]
  Cate KV is a hybrid KV cache method that retains only critical token information for consistent heads.<n>Cate KV reduces memory usage by up to $2.72times$ and accelerates decoding by $2.18times$ in single-sample inputs.
  arXiv  Detail & Related papers  (2026-08-31T06:02:37Z)
• HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval [31.594043836825403]
  Diffusion LLMs (dLLMs) improve GPU utilization over autoregressive decoding by generating multiple tokens per forward pass.<n>Their KV cache still grows linearly with context, limiting throughput at long contexts.<n>We present HERALD, a KV offloading system for block dLLMs.
  arXiv  Detail & Related papers  (2026-06-19T17:36:51Z)
• KARA: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression [3.9838043625801967]
  Reasoning language models generate long chain-of-thought (CoT)<n>CoT accumulates a massive KV cache during the decoding phase.<n> KV cache compression has emerged as a promising technique for reducing memory overhead.
  arXiv  Detail & Related papers  (2026-05-01T15:54:13Z)
• Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving [18.681651346752766]
  Dynamic sparse attention promises relief by accessing only a small, query-dependent subset of the KV state per decoding step.<n>In practice, however, these algorithmic savings rarely translate into end-to-end system-level gains.<n>We present SPIN, a sparse-attention-aware inference framework that co-designs the execution pipeline with hierarchical KV storage.
  arXiv  Detail & Related papers  (2026-04-29T16:02:00Z)
• Fast KV Compaction via Attention Matching [36.70038515897491]
  This work describes an approach for fast context compaction in latent space through Attention Matching.<n>We show that this formulation naturally decomposes into simple subproblems, some of which admit efficient closed-form solutions.
  arXiv  Detail & Related papers  (2026-02-18T09:06:53Z)
• 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)
• SpecAttn: Co-Designing Sparse Attention with Self-Speculative Decoding [3.47505166101801]
  SpecAttn is a self-speculative decoding method with verification-guided sparse attention.<n>It achieves 2.81$times$ higher throughput over vanilla auto-regressive decoding and 1.29$times$ improvement over state-of-the-art sparsity-based decoding methods.
  arXiv  Detail & Related papers  (2026-02-06T22:12:52Z)
• G-KV: Decoding-Time KV Cache Eviction with Global Attention [57.47409249054187]
  Large language models (LLMs) excel in complex tasks but encounter significant computational and memory challenges due to long sequence lengths.<n> KV cache compression has emerged as an effective approach to greatly enhance the efficiency of reasoning.<n>We propose G-KV, a KV cache eviction method that employs a global scoring mechanism, combining local and historical attention scores to more accurately assess token importance.
  arXiv  Detail & Related papers  (2025-11-29T14:21:33Z)
• Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs? [79.58770714228983]
  Language models handle increasingly long contexts for tasks such as book summarization.<n>This leads to growing memory costs for the key-value ( KV) cache.<n>Many prior works have proposed ways of discarding KVs from memory, but their approaches are tailored to favorable settings.<n>We propose the * KV footprint* as a unified metric, which accounts for both the amount of KV entries stored and their lifespan in memory.
  arXiv  Detail & Related papers  (2025-06-20T16:21:12Z)
• 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)
• ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression [10.003118268356017]
  Long context poses significant challenges for inference efficiency.<n>We introduce ClusterKV, which recalls tokens at the granularity of semantic clusters.<n>Experiment results show that ClusterKV attains negligible accuracy loss across various tasks with 32k context lengths.
  arXiv  Detail & Related papers  (2024-12-04T10:58:27Z)
• 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.