AgentKVShift: Efficient KV Cache Reuse for Agentic Memory Systems

• URL: http://arxiv.org/abs/2607.21604v1
• Date: Fri, 15 May 2026 16:29:36 GMT
• Title: AgentKVShift: Efficient KV Cache Reuse for Agentic Memory Systems
• Abstract summary: AgentKVShift is a training-free, probe-guided residual correction method that operates per retrieved memory unit.<n>We show that AgentKVShift achieves near full recompute performance while refreshing only 10-30% of the cache.<n>It requires up to 5x lower recompute to reach this near-full performance, which prior reuse methods only attain at 45-55% refresh.
• Score: 15.913507689262573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory-augmented LLM agents maintain context across hundreds of interactions through agentic memory systems that actively curate retrieved content with LLM-generated metadata such as summaries, keywords, and tags. From an inference cost standpoint, every retrieval triggers a full re-encoding of these structured memory units into Key-Value (KV) states, which dominates prefill latency. Existing training-free KV reuse methods mitigate this by selectively recomputing a small fraction of tokens, but were designed for RAG-style raw passages and degrade on structured agentic memories. We present AgentKVShift, a training-free, probe-guided KV residual correction method that operates per retrieved memory unit. One of the crucial insights we demonstrate is that the per-memory KV reuse residual decomposes into a shared memory-level offset plus small token-wise fluctuations. Estimating this offset from a small probe set allows us to correct every reused token by a single weighted correction. Unlike prior reuse methods which decide which tokens to recompute and leave the rest of the cache stale, AgentKVShift also corrects the tokens it does not recompute, turning the refresh budget into useful signal across the entire chunk. Across four open-source LLMs spanning 3B to 32B parameters and two long-horizon agentic memory benchmarks (long-term dialogue and agentic applications), AgentKVShift achieves near full recompute performance while refreshing only 10-30% of the cache, outperforming baselines at the same recompute ratio. It requires up to 5x lower recompute to reach this near-full performance, which prior reuse methods only attain at 45-55% refresh. In this regime, AgentKVShift delivers prefill speedups of 2-3.5x over no-KV-reuse on a single A100. AgentKVShift orthogonally composes with KV cache quantization, retaining over 2x the F1 of prior reuse methods under aggressive 2- and 4-bit settings.

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)
• Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior [107.2098567818173]
  Latent Recurrent Transformer (LRT) is a lightweight augmentation of autoregressive transformers.<n>LRT reuses a high-level source-layer hidden state from the previous token as recurrent memory for the next token.
  arXiv  Detail & Related papers  (2026-05-26T10:10:26Z)
• 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)
• Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators [45.88028371034407]
  KV-cache-free associative recall architecture built around Spectral Koopman Attention (SKA)<n>We introduce Echo, a KV-cache-free associative recall architecture built around Spectral Koopman Attention (SKA)
  arXiv  Detail & Related papers  (2026-05-07T22:26:27Z)
• The Residual Stream Is All You Need: On the Redundancy of the KV Cache in Transformer Inference [3.378773775514883]
  Key-value (KV) cache is widely treated as essential state in transformer inference.<n>We prove that keys and values at every layer are deterministic projections of the residual stream.<n>We build on this result with KV-Direct, a bounded-memory inference scheme.
  arXiv  Detail & Related papers  (2026-03-20T05:59:50Z)
• RelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse [5.597099794399441]
  RelayCaching is a training-free inference method that directly reuses decoding phase KV caches from previous agents.<n>We show that RelayCaching achieves over 80% KV cache reuse, reduces TTFT by up to $4.7times$ compared to the standard pipeline.
  arXiv  Detail & Related papers  (2026-02-28T04:46:28Z)
• KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning [8.216400469571084]
  We propose KEEP, a KV-cache-centric memory management system for efficient embodied planning.<n>KEEP features 3 key innovations: (1) a Static-Dynamic Memory Construction algorithm that reduces KV cache recomputation by mixed-granularity memory group; (2) a Multi-hop Memory Re-computation algorithm that dynamically identifies important cross-attention among different memory groups; and (3) a Layer-balanced Memory Loading that eliminates unbalanced KV cache loading and cross-attention across different layers.
  arXiv  Detail & Related papers  (2026-02-27T01:48:07Z)
• 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)
• 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)
• 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)
• 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)

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.