ReCache: Efficient KV Cache Reuse and Compression for Tool-Augmented LLM Agents

• URL: http://arxiv.org/abs/2608.19662v1
• Date: Thu, 20 Aug 2026 05:57:24 GMT
• Title: ReCache: Efficient KV Cache Reuse and Compression for Tool-Augmented LLM Agents
• Abstract summary: textbfReCache is a framework for independently caching resource representations.<n>We evaluate ReCache on a benchmark assembled from seven public tool- and skill-use datasets.
• Score: 7.368551130048085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agentic language models repeatedly encode tool and skill schemas that recur across requests in different combinations and orders, preventing standard prefix caching from reusing their key--value (KV) states. We introduce \textbf{ReCache}, a framework for independently caching resource representations while reducing their inference-time computational and memory overhead. Resource-wise attention removes cross-resource interactions and assigns resource-local positions, producing composition-invariant KV blocks. ReCache then restricts resource visibility to contribution-selected layer--KV-head-group routes and retains only invocation-critical fields through structural and semantic pruning. We evaluate ReCache on a benchmark assembled from seven public tool- and skill-use datasets, including resource-disjoint tests. Resource-wise attention matches dense invocation performance (82.3\% versus 82.4\% Inv-F1) while providing a 3.655$\times$ time-to-first-token speedup. The complete framework reduces allocated KV-tensor memory by 92.43\% and accelerates attention by 1.423$\times$. These results show that separating reusable schema encoding from selective resource access substantially reduces agentic inference costs with limited effectiveness loss. The code is available at https://github.com/EIT-NLP/ReCache.

Related papers

• VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference [17.932004205808486]
  VisCache is a framework for coarse-to-fine textbfVisual KV textbfCache pruning without training.<n>PruneKV is a surgical KV compression algorithm tailored to the attention dynamics of Vision Large Language Models.<n>VisCache consistently outperforms existing baselines.
  arXiv  Detail & Related papers  (2026-08-25T04:52:05Z)
• 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)
• Position Rebinding Cache Reuse: Replay-Free Visual Revisiting for Interleaved Multimodal Reasoning [51.563653495547335]
  multimodal reasoning improves visual grounding by revisiting visual evidence during multi-step generation.<n>We propose Position Rebinding Cache Reuse (PRCR), a cache-level framework for replay-free visual revisiting.<n>PRCR achieves replay-level or better performance, improving average accuracy by 5 percent and reducing visual-revisiting by up to tens of thousands of times.
  arXiv  Detail & Related papers  (2026-06-25T05:47:52Z)
• 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)
• Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving [18.829614918483255]
  AsymCache is a computation-aware KV cache management system for Large Language Model (LLM) inference.<n> Experiments show that AsymCache reduces TTFT by up to 1.90-2.03x and time-per-latency-token (TPOT) by 1.62-1.71x over latest baselines.
  arXiv  Detail & Related papers  (2026-06-01T23:51:37Z)
• CacheClip: Accelerating RAG with Effective KV Cache Reuse [8.016679032026824]
  CacheClip is a novel framework that achieves both fast TTFT and high generation quality.<n>It integrates three techniques: (1) auxiliary-model-guided token selection for selective KV cache recomputation, where the auxiliary model is finetuned to improve selection accuracy, (2) shared prefixes to eliminate redundant attention sinks, and (3) grouping strategy to maintain local coherence.
  arXiv  Detail & Related papers  (2025-10-11T09:28:26Z)
• KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse [30.48395228595732]
  We describe KVLink, an approach for efficient key-value ( KV) cache reuse in large language models (LLMs)<n> KVLink introduces two key techniques: adjusting positional embeddings of the KV cache at inference to match the global position after concatenation, and using trainable special tokens to restore self-attention.<n> Experiments across 7 datasets demonstrate that KVLink improves question answering accuracy by an average of 4% over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-02-21T23:34:29Z)
• 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)
• Efficient Inference of Vision Instruction-Following Models with Elastic Cache [76.44955111634545]
  We introduce Elastic Cache, a novel strategy for efficient deployment of instruction-following large vision-language models. We propose an importance-driven cache merging strategy to prune redundancy caches. For instruction encoding, we utilize the frequency to evaluate the importance of caches. Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation.
  arXiv  Detail & Related papers  (2024-07-25T15:29:05Z)
• Training-Free Exponential Context Extension via Cascading KV Cache [49.608367376911694]
  We introduce a novel mechanism that leverages cascading sub-cache buffers to selectively retain the most relevant tokens.<n>Our method reduces prefill stage latency by a factor of 6.8 when compared to flash attention on 1M tokens.
  arXiv  Detail & Related papers  (2024-06-24T03:59:17Z)
• Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference [78.65321721142624]
  We focus on a memory bottleneck imposed by the key-value ( KV) cache. Existing KV cache methods approach this problem by pruning or evicting large swaths of relatively less important KV pairs. We propose LESS, a simple integration of a constant sized cache with eviction-based cache methods.
  arXiv  Detail & Related papers  (2024-02-14T18:54:56Z)

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.