mlsanityData to 14 Oct 2026

Exploring a Layer-Wise Design Space for KV Cache Eviction

arXiv 2606.15157 ↗First posted 13 Jun 2026cs.LG cs.AIcited by 1

On the map: Efficient LLM inference and serving → KV cache optimization

Abstract (arXiv)

KV cache eviction methods typically use a single retention-rule family throughout a model, making eviction-method identity a model-level design choice. Yet Transformer layers differ substantially in their attention behavior, representations, and sensitivity to compression, suggesting that a uniform rule may overlook useful layer-wise structure. This raises a basic question: should eviction methods themselves vary across layers? We investigate this question by composing existing eviction methods across Transformer layers and systematically exploring the resulting layer-wise design space. Using simple offline profiles, we construct fixed routes and study how their quality varies with method placement and cache budget. On LongBench, heterogeneous routing improves performance on a majority of tasks over homogeneous policies at the same cache budget. Even when method counts are held fixed, the profile-guided placement ranks second among 100 evaluated assignments, demonstrating that routing quality depends strongly on where methods are placed. Moreover, the same fixed route outperforms the best of nine standalone baselines across all five tested cache budgets. Together, these results establish layer-wise method composition as an exploitable, placement-sensitive design dimension for KV cache compression.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in KV cache optimization, one of 12 directions in Efficient LLM inference and serving. Builds on 27 of its 29 references that are in the map. First posted on arXiv 13 Jun 2026.

Builds on

27 of 29 references.

Closest ancestors · 15

Same direction, newest first

  1. 2026MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression20 Apr
  2. 2025LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation11 Sep
  3. 2025CAKE: Cascading and Adaptive KV Cache Eviction with Layer Preferences16 Mar
  4. 2024DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs19 Dec
  5. 2024Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning25 Oct
  6. 2024DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads14 Oct
  7. 2024RazorAttention: Efficient KV Cache Compression Through Retrieval Heads22 Jul
  8. 2024Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference16 Jul
  9. 2024D2O: Dynamic Discriminative Operations for Efficient Long-Context Inference of Large Language Models18 Jun
  10. 2024PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling4 Jun
  11. 2024SnapKV: LLM Knows What You are Looking for Before Generation22 Apr
  12. 2023Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs3 Oct
3 more
  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttention12 Sep
  2. 2023H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models24 Jun
  3. 2023Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time26 May

Other references · 12

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,186
  6. 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,417
  7. 2024RULER: What's the Real Context Size of Your Long-Context Language Models?cited by 1,346
  8. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,059
  9. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 1,003
  10. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 856
  11. 2019Are Sixteen Heads Really Better than One?cited by 816
  12. 2024Transformers are Multi-State RNNscited by 120

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing
  2. 2026Discovering KV Cache Eviction Policies via LLM-Guided Program Evolution
  3. 2026Risk-Controlled KV-Cache Eviction: From Memory Budgets to Risk Targets
  4. 2026Protection Is (Nearly) All You Need: Structural Protection Dominates Scoring in Globally Capped KV Eviction
  5. 2026Every Cache Entry Earns Its Place: Global Allocation of Resolution and Coverage for KV Cache Compression
  6. 2026AvoKV-E: Payload-Aware KV Cache Eviction for Long Reasoning
  7. 2026Beyond Token Eviction: Mixed-Dimension Budget Allocation for Efficient KV Cache Compression
  8. 2026Minimal-Intervention KV Retention via Set-Conditioned Diversity