Tree Navigation Without LLM Summaries: A Matched-Cost Study of Hierarchical Retrieval for Long-Document QA

Abstract: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.

Computation and Language (cs.CL); Information Retrieval (cs.IR)

arXiv:2610.06902 [cs.CL]

(or arXiv:2610.06902v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.06902 Focus to learn more arXiv-issued DOI via DataCite

REPRODUCIBILITY.md

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results/paired_tests/2wikimultihopqa_paired.json

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results/paired_tests_openweight/qasper_paired.json

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results/paired_tests_openweight/summary_llama8b_local.json

results/paired_tests_openweight/summary_qwen7b_local.json

results/qasper_recall/qasper_recall.csv

results/qasper_recall/qasper_recall.json

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