[Submitted on 30 Sep 2026]
Title:Tree Navigation Without LLM Summaries: A Matched-Cost Study of Hierarchical Retrieval for Long-Document QA
View a PDF of the paper titled Tree Navigation Without LLM Summaries: A Matched-Cost Study of Hierarchical Retrieval for Long-Document QA, by Priyank Jayraj and 2 other authors
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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.
Subjects:
Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2610.06902 [cs.CL]
(or arXiv:2610.06902v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.06902
arXiv-issued DOI via DataCite
Submission history
From: Priyank Jayraj [view email] [v1] Wed, 30 Sep 2026 08:42:45 UTC (9,192 KB)
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README.md
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results/frozen_opt/qasper_ce/_meta/code_snapshot/faiss_utils.py
results/frozen_opt/qasper_ce/_meta/code_snapshot/inference.py
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results/frozen_opt/qasper_ce/_meta/code_snapshot/packing.py
results/frozen_opt/qasper_ce/_meta/code_snapshot/raptor_baseline.py
results/frozen_opt/qasper_ce/_meta/code_snapshot/run_benchmarks.py
results/frozen_opt/qasper_ce/_meta/code_snapshot/segtree.py
results/frozen_opt/qasper_ce/_meta/manifest.json
results/frozen_opt/qasper_ce/qasper_cells.json
results/frozen_opt/qasper_ce/qasper_ci.json
results/frozen_opt/qasper_ce/qasper_predictions.json
results/frozen_opt/qasper_ce/qasper_predictions_raw.json
results/frozen_opt/qasper_ce/qasper_results.csv
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results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/CHECKSUMS.txt
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/SNAPSHOT.txt
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/bm25.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/ce_rerank.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/faiss_utils.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/inference.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/navigated_fill.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/packing.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/raptor_baseline.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/run_benchmarks.py
results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/segtree.py
results/frozen_opt/quality_gpt4o_sota/_meta/manifest.json
results/frozen_opt/quality_gpt4o_sota/quality_cells.json
results/frozen_opt/quality_gpt4o_sota/quality_ci.json
results/frozen_opt/quality_gpt4o_sota/quality_predictions.json
results/frozen_opt/quality_gpt4o_sota/quality_predictions_raw.json
results/frozen_opt/quality_gpt4o_sota/quality_results.csv
results/frozen_opt/quality_gpt4o_sota/quality_results.json
results/frozen_opt/quality_t3/_meta/code_snapshot/CHECKSUMS.txt
results/frozen_opt/quality_t3/_meta/code_snapshot/SNAPSHOT.txt
results/frozen_opt/quality_t3/_meta/code_snapshot/bm25.py
results/frozen_opt/quality_t3/_meta/code_snapshot/ce_rerank.py
results/frozen_opt/quality_t3/_meta/code_snapshot/faiss_utils.py
results/frozen_opt/quality_t3/_meta/code_snapshot/inference.py
results/frozen_opt/quality_t3/_meta/code_snapshot/navigated_fill.py
results/frozen_opt/quality_t3/_meta/code_snapshot/packing.py
results/frozen_opt/quality_t3/_meta/code_snapshot/raptor_baseline.py
results/frozen_opt/quality_t3/_meta/code_snapshot/run_benchmarks.py
results/frozen_opt/quality_t3/_meta/code_snapshot/segtree.py
results/frozen_opt/quality_t3/_meta/manifest.json
results/frozen_opt/quality_t3/quality_cells.json
results/frozen_opt/quality_t3/quality_ci.json
results/frozen_opt/quality_t3/quality_predictions.json
results/frozen_opt/quality_t3/quality_predictions_raw.json
results/frozen_opt/quality_t3/quality_results.csv
results/frozen_opt/quality_t3/quality_results.json
results/frozen_opt/summary_vs_leaf/narrativeqa.json
results/frozen_opt/summary_vs_leaf/narrativeqa_rescored.json
results/frozen_opt/summary_vs_leaf/qasper.json
results/frozen_opt/summary_vs_leaf/results.json
results/hybrid_flat_run/multihop/2wikimultihopqa/2wikimultihopqa_checkpoint.json
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results/hybrid_flat_run/multihop/_pre_g_backup/2wikimultihopqa_checkpoint.json
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results/hybrid_flat_run/multihop/_pre_g_backup/musique_checkpoint.json
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results/hybrid_flat_run/multihop/hotpotqa/hotpotqa_results.json
results/hybrid_flat_run/multihop/hotpotqa_checkpoint.json
results/hybrid_flat_run/multihop/hotpotqa_results.csv
results/hybrid_flat_run/multihop/hotpotqa_results.json
results/hybrid_flat_run/multihop/musique/musique_checkpoint.json
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results/hybrid_flat_run/multihop/musique/musique_results.json
results/hybrid_flat_run/multihop/musique_checkpoint.json
results/hybrid_flat_run/multihop/musique_results.json
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results/hybrid_flat_run/multihop/narrativeqa_full/narrativeqa_full_results.json
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results/hybrid_flat_run/multihop/narrativeqa_full_results.json
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results/hybrid_flat_run/qasper/qasper_results.json
results/hybrid_flat_run/quality/quality_checkpoint.json
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results/hybrid_flat_run/quality/quality_results.json
results/multihop/2wikimultihopqa/2wikimultihopqa_checkpoint.json
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results/multihop/2wikimultihopqa/2wikimultihopqa_results.json
results/multihop/hotpotqa/hotpotqa_checkpoint.json
results/multihop/hotpotqa/hotpotqa_results.csv
results/multihop/hotpotqa/hotpotqa_results.json
results/multihop/musique/musique_checkpoint.json
results/multihop/musique/musique_results.csv
results/multihop/musique/musique_results.json
results/multihop/narrativeqa_full/narrativeqa_full_checkpoint.json
results/multihop/narrativeqa_full/narrativeqa_full_results.csv
results/multihop/narrativeqa_full/narrativeqa_full_results.json
results/multihop_recall/2wikimultihopqa_recall.json
results/multihop_recall/hotpotqa_recall.json
results/multihop_recall/multihop_recall.json
results/multihop_recall/musique_recall.json
results/navtree_ce_qasper/qasper_checkpoint.json
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results/navtree_ce_qasper/qasper_results.json
results/openweight_run/llama8b_local/qasper/qasper_checkpoint.json
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results/openweight_run/llama8b_local/qasper/qasper_results.json
results/openweight_run/llama8b_local/quality/quality_checkpoint.json
results/openweight_run/llama8b_local/quality/quality_results.csv
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results/openweight_run/qwen7b_local/qasper/qasper_checkpoint.json
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results/openweight_run/qwen7b_local/quality/quality_checkpoint.json
results/openweight_run/qwen7b_local/quality/quality_results.csv
results/openweight_run/qwen7b_local/quality/quality_results.json
results/paired_tests/2wikimultihopqa_paired.json
results/paired_tests/hotpotqa_paired.json
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results/paired_tests/qasper_paired.json
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results/paired_tests_openweight/summary_qwen7b_local.json
results/qasper_recall/qasper_recall.csv
results/qasper_recall/qasper_recall.json
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