When to Evict, Not What to Keep: Draft-Guided Eviction for Training-Free KV-Cache Compression
On the map: Efficient LLM inference and serving → KV cache optimization
Abstract (arXiv)
Training-free KV-cache compression methods such as SnapKV, H2O, and PyramidKV evict tokens at the end of prefill, aiming to preserve the attention mass that future queries are expected to use -optimizing what to keep. We show that this objective fails in two distinct ways. (1) Compensation: restoring the evicted attention mass can recover the attention-level target without recovering task quality. (2) Selection: covering more of the true decode-query mass can hurt quality when the recovered mass is fragmented rather than concentrated in coherent spans. These failures share a common cause: eviction occurs before the queries that determine the answer trajectory exist. We propose Draft-Guided Eviction (DGE), which defers eviction until after drafting the first k=2 answer tokens using the full cache - just one decode step beyond prefill. Because the draft is generated from the answer's own prefix, no cache entries are discarded before this trajectory signal becomes available. The per-head cache budget remains unchanged, and DGE can be applied directly to SnapKV, PyramidKV, H2O, and StreamingLLM without modifying their eviction scores. Unlike extra-pass methods, DGE changes when eviction occurs rather than what cache entries are selected. Extensive experiments demonstrate that DGE outperforms prior methods at every evaluated budget on five of six instruct-tuned backbones, achieving 44.2 on LongBench, nearly matching FullKV at 44.3. The timing-only control DGE-W achieves the same score, demonstrating that the gain comes from when eviction occurs rather than what is selected - an effect we term trajectory anchoring.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in KV cache optimization, one of 12 directions in Efficient LLM inference and serving. Builds on 45 of its 50 references that are in the map. First posted on arXiv 27 Sep 2026.
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45 of 50 references.
Closest ancestors · 20
Same direction, newest first
- 2026When Does Value-Aware KV Eviction Help? A Fixed-Contract Diagnostic for Non-Monotone Cache Compression7 May
- 2026Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective28 Apr
- 2026LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation11 Mar
- 2025KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction29 May
- 2025Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query24 May
- 2025CriticalKV: Optimizing KV Cache Eviction from an Output Perturbation Perspective6 Feb
- 2024Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning25 Oct
- 2024ThinK: Thinner Key Cache by Query-Driven Pruning30 Jul
- 2024Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference16 Jul
- 2024Model Tells You Where to Merge: Adaptive KV Cache Merging for LLMs on Long-Context Tasks11 Jul
- 2024InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management28 Jun
- 2024LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference26 Jun
8 more
- 2024A Simple and Effective L2 Norm-Based Strategy for KV Cache Compression17 Jun
- 2024Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference16 Jun
- 2024PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling4 Jun
- 2024SnapKV: LLM Knows What You are Looking for Before Generation22 Apr
- 2023Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs3 Oct
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttention12 Sep
- 2023H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models24 Jun
- 2023Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time26 May
Other references · 25
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2024Qwen2.5 Technical Reportcited by 4,713
- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
- 2023Mistral 7Bcited by 3,065
- 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
- 2023Efficient Streaming Language Models with Attention Sinkscited by 2,115
- 2022Fast Inference from Transformers via Speculative Decodingcited by 1,615
- 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,378
- 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
- 2022Token Merging: Your ViT But Fastercited by 995
- 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
- 2024KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cachecited by 604
13 more
- 2017The NarrativeQA Reading Comprehension Challengecited by 590
- 2024KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantizationcited by 571
- 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 530
- 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
- 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
- 2023Learning to Compress Prompts with Gist Tokenscited by 379
- 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 363
- 2023RepoBench: Benchmarking Repository-Level Code Auto-Completion Systemscited by 346
- 2021Efficient Attentions for Long Document Summarizationcited by 342
- 2024Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inferencecited by 115
- 2023LongCoder: A Long-Range Pre-trained Language Model for Code Completioncited by 110
- 2024Long Context Compression with Activation Beaconcited by 101
- 2026Fast KV Compaction via Attention Matchingcited by 28
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