5. VideoRAG & V-RAGBench
5. VideoRAG 与 V-RAGBench
本文提出 V-RAGBench——一个由 ⟨query, evidence chunk, answer⟩ 三元组构成的基准，可对检索与生成进行忠实且解耦的评估；同时提出 CARVE，一种在多种配置下并行运行检索器、并通过分块自适应重排序为每个分块挑选最优配置的简易方法。V-RAGBench is introduced, a benchmark of $\langle$ query, evidence chunk, answer$\rangle$ triplets that enables faithful, decoupled evaluation of retrieval and generation, and CARVE, a simple method that runs parallel retrievers across configurations and employs chunk-adaptive reranking to identify the winning configuration for each chunk.
1️⃣2️⃣ arXiv · RAGPerf: End-to-End RAG Benchmarking Framework（⭐⭐⭐ 参考）
arXiv · RAGPerf：端到端 RAG 基准测试框架（⭐⭐⭐ 参考）
提出一个面向 RAG 的 AI 系统基准测试（RAGPerf）框架，用于刻画 RAG pipeline 的系统行为，并证明其引入的性能开销可忽略不计。The design and implementation of a RAG-based AI system benchmarking (RAGPerf) framework for characterizing the system behaviors of RAG pipelines is presented and it is shown that RAGPerf incurs negligible performance overhead.
5️⃣ arXiv · Is Agentic RAG Worth It? An Experimental Comparison of RAG Approaches（⭐⭐⭐⭐ 高优先级）
5️⃣ arXiv · Agentic RAG 是否值得？RAG 方法的实验对比（⭐⭐⭐⭐ 高优先级）
基于实证对 "Enhanced" 与 "Agentic" RAG 范式进行评估，为真实场景中选取最有效的 RAG 设计（兼顾性能与成本）提供指导。An empirically driven evaluation of the "Enhanced" and "Agentic" RAG paradigms is conducted, offering guidance on selecting the most effective RAG design for real-world applications, considering both performance and costs.