Retrieval benchmarks are increasingly saturating, but we argue that efficient search is far from a solved problem. We identify a class of queries we call oblique, which seek documents that instantiate a latent pattern, like finding all tweets that express an implicit stance, chat logs that demonstrate a particular failure mode, or transcripts that match an abstract scenario. We study three mechanisms through which obliqueness may arise and introduce OBLIQ-Bench, a suite of five oblique search problems over real long-tail corpora. OBLIQ-Bench exposes an overlooked asymmetry between retrieval and verification, where reasoning LLMs reliably recognize latent relevance whenever relevant documents are surfaced, but even sophisticated retrieval pipelines fail to surface most relevant documents in the first place. We hope that OBLIQ-Bench will drive research into retrieval architectures that efficiently capture latent patterns and implicit signals in large corpora.

翻译：检索基准测试正日益饱和，但我们认为高效搜索远非一个已解决的问题。我们识别出一类名为“斜向查询”的查询，该类查询旨在检索体现潜在模式的文档，例如寻找所有表达隐性立场的推文、展现特定故障模式的聊天记录，或匹配抽象场景的转录文本。我们研究了斜向性产生的三种机制，并提出了OBLIQ-Bench——一套基于真实长尾语料库的五项斜向检索问题集合。OBLIQ-Bench揭示了检索与验证之间被忽视的不对称性：当相关文档被呈现时，推理型LLM能可靠地识别潜在相关性，但即便是最复杂的检索流程，也往往无法在初始阶段将大部分相关文档加以呈现。我们希望OBLIQ-Bench能推动针对能够高效捕获大型语料库中潜在模式与隐性信号的检索架构的研究。