Work on KV cache compression, quantization, eviction, and prefill/decode splitting has exploded in the past two years, but most of it self-reports gains on isolated benchmarks. The survey "Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization" (arXiv:2607.08057), accepted into ACL 2026 Findings, is the first to pull this "algorithm race" back into a systems-engineering context. The paper's core is the sKis framework (system-aware KV infrastructure for serving LLMs), classifying existing methods along three dimensions: the time axis covers scheduling, pipelining, and hardware-aware execution; the space axis splits layout and migration into GPU memory hierarchy and cross-compute-device layers; the structure axis covers quantization, low-rank approximation, structural compression, eviction strategies, and lifecycle management (KVCC / KVRM). The more valuable piece is the accompanying behavior × objective matrix — the table explicitly labels whether each method primarily improves average latency, tail latency, throughput, memory, or interconnect I/O, and lists "quality loss" as an independent dimension. The study points out that ≥70% of existing papers only report two of these benefits, with almost no mention of interconnect contention, energy, or quality impact. For engineering teams, sKis provides a "select technique by bottleneck" decision tool: when deployment is stuck on memory, flip to §5.1; when stuck on cross-card bandwidth, go back to §4.2. If the next KV paper doesn't locate itself in this matrix, the perspective is clearly narrow.