BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference

• URL: http://arxiv.org/abs/2609.04971v1
• Date: Fri, 04 Sep 2026 10:23:16 GMT
• Title: BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference
• Abstract summary: 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.
• Score: 20.58039191111583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generation, but the resulting key-value (KV) cache grows linearly with sequence length and creates severe memory bottlenecks, often exceeding GPU capacity for long reasoning traces. Existing KV cache compression methods rely on recent queries to estimate future token importance, implicitly assuming these serve as reliable proxies for future attention patterns. We demonstrate that this assumption fails in long-horizon reasoning: certain decoding steps generate Thought Revisiting Tokens (TRT) that re-attend to distant previous context, such as task-solving plans formulated early in the trace. Through systematic analysis, we discover that queries corresponding to the TRT cluster into a small number of similarity groups in the embedding space. Based on this insight, we propose BeaconKV, a training-free KV cache compression method that maintains beacon queries, compact representatives for each global query cluster, to anticipate which KV pairs will be revisited without storing the entire query history. Across four open-source LRMs and diverse reasoning benchmarks, BeaconKV generally outperforms existing compression methods, achieving up to $5.8\times$ memory reduction while nearly preserving full cache accuracy and improving throughput by over $4.3\times$.

Related papers

• Back from the Future: Key-Value Cache Management by Counter-Causal Surprise [67.1056509495879]
  Key-value (KV) cache management through compression and eviction strategies has emerged as an important research direction in recent years.<n>We propose a simple yet effective KV eviction scheme motivated by the insight that past tokens which can be well-predicted from more recent tokens are redundant.<n>We evaluate our strategy on various open-source LLMs and benchmark datasets showing competitive or improved performance over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-30T02:42:51Z)
• CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference [9.416125453771361]
  Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value caches.<n>We propose CompressKV, a resource-efficient KV-cache compression framework for GQA-based LLMs.<n>Experiments on LongBench and Needle-in-a-Haystack show that CompressKV consistently outperforms existing KV-cache eviction methods across memory budgets.
  arXiv  Detail & Related papers  (2026-06-23T11:59:46Z)
• GRKV: Global Regression for Training-Free KV Cache Compression in Long-Context LLMs [97.36238579001544]
  Large language models (LLMs) with extended context lengths rely on the key-value ( KV) cache to support attention over prior tokens.<n>Maintaining the KV cache incurs substantial memory overhead, motivating KV-cache compression methods.<n>We propose GRKV, a training-free KV-cache merging method that directly minimizes the discrepancy between compressed-cache and full-cache attention outputs.
  arXiv  Detail & Related papers  (2026-05-29T10:16:30Z)
• Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction [65.710271475739]
  We introduce a global retention-based KV eviction method that learns each token's future utility under a unified memory budget.<n>We show that our method substantially reduces KV memory while matching or surpassing full-cache inference.<n>These results suggest that learned, globally calibrated KV eviction is not only a compression technique, but also a mechanism for improving long-context reasoning.
  arXiv  Detail & Related papers  (2026-05-10T16:47:50Z)
• Kwai Summary Attention Technical Report [69.40814939510126]
  Long-context ability, has become one of the most important direction of next-generation Large Language Models.<n>Standard softmax attention exhibits quadratic time complexity with respect to sequence length.<n>We propose Kwai Summary Attention (KSA), a novel attention mechanism that reduces sequence modeling cost by compressing historical contexts.
  arXiv  Detail & Related papers  (2026-04-27T12:59:53Z)
• 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)
• Expected Attention: KV Cache Compression by Estimating Attention from Future Queries Distribution [2.894551569099569]
  We introduce $textbfExpected Attention, a training-free compression method that estimates KV pairs importance by predicting how future queries will attend to them.<n>Our method operates seamlessly across both prefilling and decoding phases, consistently outperforming state-of-the-art baselines in both scenarios.<n>$textbfwe release KVPress, a comprehensive library to enable researchers to implement and benchmark KV cache compression methods.
  arXiv  Detail & Related papers  (2025-10-01T08:12:14Z)
• ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models [13.284627477293322]
  ThinKV is a thought-adaptive KV cache compression framework.<n>It applies a hybrid quantization-eviction strategy, assigning token precision by thought importance.<n>Experiments on DeepSeek-R1-Distill, GPT-OSS, and NVIDIA AceReason show that ThinKV achieves near-lossless accuracy with less than 5% of the original KV cache.
  arXiv  Detail & Related papers  (2025-10-01T04:09:02Z)
• 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)
• 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.