发表机构
Birla Institute of Technology and Science, Pilani（皮拉尼比尔拉理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出NavTree，一种零LLM索引成本的仅叶分层检索器，通过确定性分段树导航，在匹配成本下超越RAPTOR式摘要树，并在长文档多跳问答中显著优于BM25。
AI 中文摘要
检索增强生成将语言模型置于外部上下文中，但对于长文档，平坦的top-$k$检索可能会聚集在单一区域并遗漏互补证据。RAPTOR风格的摘要树通过递归聚类块并在索引时使用语言模型对每个聚类进行摘要，然后在查询时将摘要节点与原始块一起排序来解决这一问题。我们表明，在长文档问答中，摘要树的主要好处可能来自导航而非生成的摘要内容。我们引入NavTree，一种仅叶检索器，它在块上构建确定性平衡分段树（索引时零语言模型调用），并仅将树用作导航支架：一种混合词汇和稠密的前沿遍历，锚定在顶部检索的叶子上，从根下降并仅向读取器发出叶块。在与平坦检索器和RAPTOR的抽取式重新实现的匹配成本评估中，NavTree是我们评估网格中最强的匹配成本分层检索器，并与最强的平坦基线持平。在长文档多跳问答中，它是唯一在类对类基础上显著优于BM25的分层方法，并通过无读取器的检索召回检查得到证实。在给定强簇摘要的情况下，对已发表的抽象式RAPTOR变体的匹配读取器复制，在每个多块预算下仍输给NavTree，且索引成本为零。排名在更强和开放权重的读取器、更强的编码器以及一个将仅叶发射隔离为结构杠杆的全因子设计中保持一致。
英文摘要
Retrieval-augmented generation grounds language models in external context, but for long documents flat top-$k$ retrieval can cluster on a single region and miss complementary evidence. RAPTOR-style summary trees address this by recursively clustering chunks and using a language model to summarize each cluster at indexing time, then ranking summary nodes alongside raw chunks at query time. We show the main benefit of summary trees in long-document QA can come from navigation rather than the generated summary content. We introduce NavTree, a leaves-only retriever that builds a deterministic balanced segment tree over chunks (zero language-model calls at indexing) and uses the tree purely as a navigation scaffold: a hybrid lexical-and-dense frontier walk, anchored on top retrieved leaves, descends from the root and emits only leaf chunks to the reader. On a matched-cost evaluation against flat retrievers and an extractive re-implementation of RAPTOR, NavTree is the strongest matched-cost hierarchical retriever in our evaluated grid and ties the strongest flat baseline. On long-document multi-hop QA, it is the only hierarchical method that significantly beats BM25 on a class-vs-class basis, corroborated by a reader-free retrieval-recall check. A matched-reader replication of the published abstractive RAPTOR variant, given strong cluster summaries, still loses to NavTree at every multi-chunk budget, at zero indexing cost. The ranking carries across stronger and open-weight readers, a stronger encoder, and a full factorial that isolates leaves-only emission as the structural lever.
