AI搜文档像挑菜：光看单颗没用，得看整把
现在的AI搜文档，就像只看每颗菜新不新鲜，不管整把菜里有没有重复、冲突、互补。这篇论文发现，即使最好的重排序方法，对文档集的覆盖度也不到45%，跨文档协调能力普遍弱。他们提出一个不用训练的方法，把评价标准直接转成选文档集的信号，用更少的文档和搜索轮次就达到最好的下游生成效果。它不是你明天能用上的，但告诉你：AI搜资料的下一个战场，是从挑单篇变成挑整组。
📄 原文摘要(英文)
As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream generation. Yet existing evaluation systems remain confined to scoring documents independently and aggregating via nDCG, ignoring inter-document interactions (redundancy, conflict, complementarity) and unable to answer what makes one document set better than another. To address these issues, we propose a complete evaluate-diagnose-optimize framework. We design SetwiseEvalKit, a three-level, nine-dimension document set evaluation benchmark covering both short-form and long-form scenarios, comprising approximately 28K high-quality evaluation rubrics. We systematically evaluate 12 rerankers: even the best method achieves no more than 45% coverage, cross-document coordination dimensions are universally weak, and no single method maintains top performance across both settings. Building on this, we propose Rubric4Setwise, a training-free method that converts rubric-based evaluation criteria into document set selection signals, achieving the best downstream generation performance with fewer documents and search rounds. It is the only method that maintains state-of-the-art results across both scenarios, validating the effectiveness of closing the loop from evaluation to optimization.