CAGE: An Evaluation Framework for Cache-Augmented Generation Models - Carmo, Lucas Mariano do; Brandão, Wladmir Cardoso; Freitas, Henrique Cota de (Anais do Workshop em Desempenho de Sistemas Computacionais e de Comunicação (WPerformance); 2026: Anais do XXV Workshop em Desempenho de Sistemas Computacionais e de ComunicaçãoDO - 10.5753/wperformance.2026.23603, 2026)
Cache-Augmented Generation (CAG) is an emerging design that reduces the cost of repeated prompt processing by reusing previously processed context, yet it lacks a standard evaluation approach. We present CAGE, a framework that combines serving metrics and semantic quality analysis across baselines in cache-aware AI systems. CAGE integrates features from vLLM’s native prefix caching and evaluates latency, TTFT, throughput, and semantic metrics. In our results, native prefix caching reduced latency of 37.4% and TTFT by 65.7% with no loss in faithfulness, whereas RAG increased latency by 70.4% and reduced faithfulness by 11.6%. These results validate the usefulness of CAGE as an approach for evaluating cache-aware LLM systems.