Probing the Prompt KV Cache: Where It Becomes Dispensable
- URL: http://arxiv.org/abs/2605.30574v1
- Date: Thu, 28 May 2026 21:07:41 GMT
- Title: Probing the Prompt KV Cache: Where It Becomes Dispensable
- Abstract summary: Prior KV cache compression schemes empirically demonstrate that the prompt cache is partially redundant during decoding, dropping or summarising entries with little accuracy loss.<n>We ask when and what kind of redundancy: at which layers, after how many decoding steps, and in what form can the prompt span KV cache be replaced without breaking the task.<n>A controlled splice intervention swept over layer cutoff and decoding steps shows this redundancy is about form (chat template scaffolding) rather than content.
- Score: 9.529147118376464
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Prior KV cache compression schemes empirically demonstrate that the prompt cache is partially redundant during decoding, dropping or summarising entries with little accuracy loss. We ask when and what kind of redundancy: at which layers, after how many decoding steps, and in what form can the prompt span KV cache be replaced without breaking the task. A controlled splice intervention swept over layer cutoff and decoding steps shows this redundancy is about form (chat template scaffolding) rather than content. Replacing the upper layer prompt span KV cache with KV cache from a chat template scaffold whose user content is a neutral filler recovers near clean accuracy, while zeroing the same slots collapses accuracy. The dissociation replicates across the Qwen3, Gemma 3, and Llama 3 families on multiple datasets.
Related papers
- KVShareArena: KV-Cache Reuse Across Contexts and Model Checkpoints [14.86076891340077]
KVShare-cache reuse across prompt contexts and model checkpoints on retrieved chunks and agent reports.<n>We find that correcting positions, which needs no recomputation, is enough until a question needs several sources at once.<n> KVArena-cache scores every method by the fraction of the gap it recovers between no cache and full recomputation.
arXiv Detail & Related papers (2026-09-09T14:53:45Z) - SemPIC: Learning Semantic Position-Independent KV Caches [26.14495003304512]
emphSemPIC trains a LoRA-enabled Writer to compile native per-layer document KVs through behavioral distillation.<n> KV Gradient Checkpointing, which reduces peak training memory without severing gradients through cached KVs.
arXiv Detail & Related papers (2026-07-30T11:45:24Z) - 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) - MuKV: Multi-Grained KV Cache Compression for Long Streaming Video Question-Answering [75.0394545769057]
KV-caching stores the Key-Value of the historical tokens via LLM prefill.<n>MuKV is a method that features a multi-grained KV cache compression module and a semi-hierarchical retrieval approach.<n> Experiments on long-streaming VideoQA benchmarks show that MuKV significantly improves answer accuracy, without sacrificing memory and online QA efficiency.
arXiv Detail & Related papers (2026-05-21T10:13:03Z) - PackCache: A Training-Free Acceleration Method for Unified Autoregressive Video Generation via Compact KV-Cache [61.57938553036056]
We introduce PackCache, a training-free KV-cache management method that compacts the KV cache through three coordinated mechanisms.<n>In terms of efficiency, PackCache accelerates end-to-end generation by 1.7-2.2x on 48-frame long sequences.
arXiv Detail & Related papers (2026-01-07T19:51:06Z) - 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) - Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction [53.83828564664595]
Large language models (LLMs) utilize key-value ( KV) cache to store historical information during sequence processing.<n>Current methods for KV cache eviction typically utilize the last window from the pre-filling phase as queries to compute the KV importance scores for eviction.<n>We propose Judge Q, a novel training method which incorporates a soft token list.
arXiv Detail & Related papers (2025-09-13T03:34:12Z) - 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) - KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing [58.29726147780976]
We propose a plug-and-play method called textit KVSharer, which shares the KV cache between layers to achieve layer-wise compression.
Experiments show that textit KVSharer can reduce KV cache computation by 30%, thereby lowering memory consumption.
We verify that textit KVSharer is compatible with existing intra-layer KV cache compression methods, and combining both can further save memory.
arXiv Detail & Related papers (2024-10-24T08:06:41Z) - CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion [15.344568214955688]
Large language models (LLMs) often incorporate multiple text chunks in their inputs to provide the necessary contexts.<n>To speed up the prefill of the long LLM inputs, one can pre-compute the KV cache of a text and re-use the KV cache when the context is reused as the prefix of another LLM input.<n>This paper tackles just one challenge: how to quickly combine their precomputed KV caches in order to achieve the same generation quality as the expensive full prefill.
arXiv Detail & Related papers (2024-05-26T06:00:17Z) - 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) - CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving [31.766738294505767]
CacheGen is a fast context-loading module for large language models.
Uses a custom tensor encoder to encode a KV cache into compact bitstream representations.
adapts the compression level of different parts of a KV cache to cope with changes in available bandwidth.
arXiv Detail & Related papers (2023-10-11T07:08:20Z)
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.