规模越大，BM25越赢：检索增强的意外反转
大家都以为AI检索该用更聪明的办法，但这项对照实验发现：当语料库从最小规模放大450倍，最朴素的BM25关键词检索反而一路反超，在最大规模时领先近20个百分点。研究者把28个严格嵌套的语料规模、同一批问题和文档喂给不同检索范式，发现没有永远的赢家：文件系统智能体在小规模领先，但它的顺序探索在最大规模时多花39倍查询token，效果还变差；图检索在部署前就撞上构建墙；稠密检索一直高效但不够准。结论很直接：语料越大，全局候选排序越占优，关键词检索是最能扛规模的默认选项，而智能体推理最好用在排序之后，而不是替代排序。
📄 原文摘要(英文)
Retrieval-augmented generation (RAG) spans lexical and dense retrieval, graph-based indexing, and agentic search, but these paradigms are usually evaluated on different benchmarks at one corpus size, leaving their accuracy-cost scaling unclear. To bridge this gap, we present a controlled study that varies corpus size along 28 strictly nested tiers spanning roughly 450-fold, while holding questions and a fixed bedrock of relevant and adversarial documents unchanged. Under one reader model and one judging protocol, we measure official accuracy, construction and query tokens, and latency. The results reveal a scale-dependent crossover rather than an unconditional winner. File-System Agent leads at the smallest shared tiers, but its sequential exploration costs 39 times more query tokens at the bedrock and becomes less effective as the search space grows. Around 10 million corpus tokens, BM25 overtakes it and leads at every larger shared tier, with a margin approaching 20 points at full scale. BM25 also anchors the low-cost end of the Pareto frontier without LLM-based construction. Dense retrieval remains efficient but less accurate, whereas graph-based RAG encounters construction walls before deployment scale and its scalable variants remain below BM25 at shared tiers. Overall, corpus growth increasingly favors global candidate ranking: lexical retrieval is the strongest scalable default, while agentic reasoning works best after ranked discovery rather than in place of it.