Which Eviction Policy Should an LLM Cache Use? A Systematic Study Across Workloads, Capacities, and Encoders
Score
77首次统一对比多种语义缓存逐出策略，并揭示LFU的优越性与答案可替代性这一此前被忽视的关键问题。
实验设计系统覆盖多工作负载、容量与编码器，且给出了条件填充的理论解释，论证较为扎实。
为LLM缓存工程部署提供了明确可执行的建议：先验证答案有效性，再以LFU为默认策略。
LLM推理成本优化是当前热点，语义缓存策略研究具有及时的现实意义。
来自arXiv AI/CL/LG预印本，尚未经同行评审，但方法透明且基于公开数据集。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper fills the gap of missing unified eviction policy comparisons for semantic caches and offers counterintuitive yet actionable engineering guidance.
AI commentary
「The biggest takeaway is counterintuitive but practical: don't over-engineer eviction policies; first check whether your cache hits are truly usable. For engineers working on LLM inference optimization and infrastructure, this paper reminds you that quality-adjusted hit rate is the real KPI, and it makes a strong case for LFU as a default.」
Quick overview
This paper presents the first systematic comparison of eviction policies for LLM semantic caches under a unified protocol. Using CLEVER, the authors evaluate FIFO, LRU, LFU, ARC, GDSF, a streaming SISO variant, and a semantic-redundancy policy across three query corpora, three cache capacities, and two encoders. No policy beats LFU by more than 0.041 percentage points in any of the eighteen settings, but at tight capacity FIFO and streaming SISO trail LFU by up to 8.67 and 8.55 points. A conditional packing result explains that geometry-aware eviction rules receive little new redundancy signal under exact lookup and insert-on-miss. The audit further shows that only 2.1%-3.9% of sampled hits are answer-substitutable at MiniLM's median nearest-neighbor threshold, dropping quality-adjusted hit rates from 51%-60% to 1.1%-2.2%. The authors conclude that LFU is the strongest simple default and that deployments should first establish answer validity before testing sub-point policy differences with exact search.
- No eviction policy outperforms LFU by more than 0.041 percentage points across all eighteen settings, making LFU the strongest simple default.
- At tight capacity, FIFO and streaming SISO trail LFU by up to 8.67 and 8.55 points, showing replacement is not irrelevant.
- Under exact lookup and insert-on-miss, a newly inserted entry cannot have a resident neighbor within the hit radius, limiting the redundancy signal for geometry-aware eviction.
- At MiniLM's median nearest-neighbor threshold, only 2.1%-3.9% of sampled hits are answer-substitutable, dropping quality-adjusted hit rates from 51%-60% to 1.1%-2.2%.
Full text
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