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.
Ancillary-file links:
Ancillary files (details):
- code/asrmb/baselines/bm25.py
- code/asrmb/baselines/dense_flat.py
- code/asrmb/baselines/hybrid_flat.py
- code/asrmb/baselines/raptor_baseline.py
- code/asrmb/benchmarks/__init__.py
- code/asrmb/benchmarks/base.py
- code/asrmb/benchmarks/multihop.py
- code/asrmb/benchmarks/narrativeqa.py
- code/asrmb/benchmarks/qasper.py
- code/asrmb/benchmarks/quality.py
- code/asrmb/core/__init__.py
- code/asrmb/core/inference.py
- code/asrmb/core/packing.py
- code/asrmb/core/segtree.py
- code/asrmb/index/__init__.py
- code/asrmb/index/faiss_utils.py
- code/asrmb/retrieval/__init__.py
- code/asrmb/retrieval/ce_rerank.py
- code/asrmb/retrieval/navigated_fill.py
- code/requirements.txt
- code/scripts/bench_index_cost.py
- code/scripts/bench_query_latency.py
- code/scripts/build_anon_zip.sh
- code/scripts/build_paper_figures.py
- code/scripts/build_paper_tables.py
- code/scripts/compute_confidence_intervals.py
- code/scripts/diagnostics/qasper_tail_token_stats.py
- code/scripts/exp_abstractive_raptor.py
- code/scripts/exp_alpha_kleaf_sweep.sh
- code/scripts/exp_factorial_w7_quality2000.sh
- code/scripts/exp_hybrid_flat_multihop_fill.sh
- code/scripts/exp_multi_seed.sh
- code/scripts/exp_multihop.sh
- code/scripts/exp_queue_remaining.sh
- code/scripts/exp_summary_vs_leaf.py
- code/scripts/exp_summary_vs_leaf_extractive.py
- code/scripts/exp_winning_cell_ablation.sh
- code/scripts/merge_hybrid_flat_multihop.py
- code/scripts/paper_status.sh
- code/scripts/phase1_build_contexts.py
- code/scripts/phase1_longdoc.py
- code/scripts/phase2_longdoc.py
- code/scripts/phase2_run_reader.py
- code/scripts/plot_results.py
- code/scripts/post_qwen_qasper_finalize.sh
- code/scripts/render_appendix_table.py
- code/scripts/render_factorial_w7.py
- code/scripts/render_table_multihop_recall.py
- code/scripts/render_table_openweight.py
- code/scripts/render_table_qasper_recall.py
- code/scripts/rescore_nqa_summary_leaf.py
- code/scripts/resume_arr_experiments.sh
- code/scripts/run_a1_ollama.sh
- code/scripts/run_a1_openweight.sh
- code/scripts/run_a3_bge_large.sh
- code/scripts/run_ablation_qasper256.sh
- code/scripts/run_benchmarks.py
- code/scripts/run_canonical_baseline.sh
- code/scripts/run_multihop_recall.py
- code/scripts/run_niah.py
- code/scripts/run_niah_multi.py
- code/scripts/run_overnight_orchestrator.sh
- code/scripts/run_paired_tests.py
- code/scripts/run_paired_tests_openweight.py
- code/scripts/run_qasper_recall.py
- code/scripts/sync_asrmb_paper_tables.py
- code/scripts/sync_quality_asrmb_tables_from_canon.py
- responsible_nlp_checklist.md
- results/abstractive_raptor_gpt4o/quality_checkpoint.json
- results/abstractive_raptor_gpt4o/quality_predictions.json
- results/abstractive_raptor_gpt4o/quality_results.json
- results/abstractive_raptor_gpt4o/summary.json
- results/bge_large_run/qasper/qasper_checkpoint.json
- results/bge_large_run/qasper/qasper_results.csv
- results/bge_large_run/qasper/qasper_results.json
- results/bge_large_run/quality/quality_checkpoint.json
- results/bge_large_run/quality/quality_results.csv
- results/bge_large_run/quality/quality_results.json
- results/canonical_v1/narrativeqa/narrativeqa_checkpoint.json
- results/canonical_v1/narrativeqa/narrativeqa_results.csv
- results/canonical_v1/narrativeqa/narrativeqa_results.json
- results/canonical_v1/qasper/qasper_checkpoint.json
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- results/canonical_v1/qasper/qasper_results.json
- results/canonical_v1/quality/quality_checkpoint.json
- results/canonical_v1/quality/quality_results.csv
- results/canonical_v1/quality/quality_results.json
- results/factorial_w7_quality2000/A0_B0_C0_D0/quality_checkpoint.json
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- results/factorial_w7_quality2000/A0_B0_C1_D1/quality_checkpoint.json
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- results/factorial_w7_quality2000/_summary.json
- results/final_archive/README.md
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- results/frozen_opt/quality_gpt4o_sota/_meta/code_snapshot/ce_rerank.py
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- results/openweight_run/llama8b_local/qasper/qasper_checkpoint.json
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- results/paired_tests/2wikimultihopqa_paired.json
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- results/paired_tests_openweight/qasper_paired.json
- results/paired_tests_openweight/quality_paired.json
- 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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