S$^4$R: Selective Sampling, Subspaces, and Sparse Reconstruction for Compressed Long-Context KV Caching

• URL: http://arxiv.org/abs/2608.00528v1
• Date: Sat, 01 Aug 2026 08:41:29 GMT
• Title: S$^4$R: Selective Sampling, Subspaces, and Sparse Reconstruction for Compressed Long-Context KV Caching
• Abstract summary: We propose S$4$R, which builds low-rank subspaces from selectively sampled tokens and computes attention over a sparsely reconstructed KV representation.<n>Experiments on LongBench and RULER with Llama and Qwen model families show that S$4$R achieves up to 5$times$ KV compression with near full-cache accuracy.
• Score: 38.29032214906932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growth of context window lengths in Large Language Models (LLMs) significantly enhances their long-context capabilities but incurs prohibitive memory costs due to the Key-Value (KV) cache. Although low-rank compression of KV cache is a promising remedy, existing methods face a dilemma: offline approaches depend on external calibration data, whereas online approaches incur substantial compute for full-prompt decomposition and reconstruction. In this paper, we propose S$^4$R, which builds low-rank subspaces from selectively sampled tokens and computes attention over a sparsely reconstructed KV representation. S$^4$R uses prompt-aware initialization to build initial key/value bases from a representative prompt subset, trading off calibration-data dependence against prefilling cost. Because fully reconstructing the cache at every decoding step is prohibitively expensive and hurts throughput, we further adopt sparse reconstruction to retain only informative positions during decoding. Extensive experiments on LongBench and RULER with Llama and Qwen model families show that S$^4$R achieves up to 5$\times$ KV compression with near full-cache accuracy, combining the efficiency of fixed compression with the adaptability of prompt-dependent methods.

Related papers

• BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference [20.58039191111583]
  Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generation.<n>The resulting key-value ( KV) cache grows linearly with sequence length and creates severe memory bottlenecks.<n>We propose BeaconKV, a training-free KV cache compression method that maintains beacon queries, compact representatives for each global query cluster.
  arXiv  Detail & Related papers  (2026-09-04T10:23:16Z)
• RestoreKV: Recovering Full-Cache Behavior Under Aggressive Query-Agnostic KV Cache Eviction [10.668136973950453]
  KV cache eviction compresses a context once and reuses the resulting cache for arbitrary future queries, but performance can collapse under tight budgets.<n>We introduce RestoreKV, which complements this selection-based formulation with learned restoration under the same total KV budget.<n>Our key insight is that, although the information lost through eviction is context-specific, the mechanism for generating its compact complement can be shared across contexts.
  arXiv  Detail & Related papers  (2026-08-02T13:58:42Z)
• C$^2$KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference [31.428047145437773]
  Long-context inference is central to modern large language model (LLM) applications such as retrieval-augmented generation and multi-document reasoning.<n>To mitigate the growing inference cost, recent work has explored key-value ( KV) cache reuse to reduce redundant prefill computation.<n>Existing reuse methods primarily focus on savings and overlook a critical bottleneck in long-context serving: the cost of storing and accessing KV caches.
  arXiv  Detail & Related papers  (2026-07-20T09:09:23Z)
• 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)
• KVReviver: Reversible KV Cache Compression with Sketch-Based Token Reconstruction [20.53279247581787]
  We propose KVReviver, a reversible KV cache compression method based on the sketch algorithm.<n>In 2k-length contexts, it requires only 10% of KV Cache budget while maintaining identical end-to-end inference accuracy.<n>For 32k-length contexts, it achieves equivalent or comparable accuracy 2% accuracy loss.
  arXiv  Detail & Related papers  (2025-12-01T03:59:20Z)
• 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)
• 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)
• 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.