{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A product assistant retrieves plausible documents but gives unsupported answers.",
    "change": "Change one retrieval or ranking step while keeping the document snapshot fixed.",
    "baseline": "Your current retrieval pipeline with identical questions and answer model.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Supported answers, retrieval misses, correct abstentions, index cost, and response latency.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.11370",
  "title": "RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees",
  "summary": "RIT-RAG replaces flat chunk retrieval and single-document structure navigation (e.g., PageIndex) with a hybrid: broad chunk retrieval induces sub-trees from per-document table-of-contents/sitemap trees, which an LLM agent then navigates and selectively reads. Costs include an offline tree-construction pipeline per document, additional LLM agent calls for navigation, node reading, and query reformulation (higher latency and API spend per query than vanilla RAG), plus engineering complexity in maintaining document-structure extraction. Failure modes include documents lacking reliable TOCs or sitemaps, retrieval proposing poor starting positions that mislead the induced sub-tree, and agent navigation errors accumulating over multiple steps, which may not justify the gains on small or weakly structured corpora.",
  "rationale": "The accuracy gains on large technical-documentation corpora are relevant to the reader's production RAG work and achievable with API-based LLMs, but the offline tree pipeline and agent-loop overhead require validation on the reader's specific corpus structure and latency budget before adoption.",
  "technique": "Retrieval-induced tree navigation for RAG",
  "family": "recuperacao_e_rag",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11370",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 4,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RIT-RAG replaces flat chunk retrieval and single-document structure navigation (e.g., PageIndex) with a hybrid: broad chunk retrieval induces sub-trees from per-document table-of-contents/sitemap trees, which an LLM agent then navigates and selectively reads. Costs include an offline tree-construction pipeline per document, additional LLM agent calls for navigation, node reading, and query reformulation (higher latency and API spend per query than vanilla RAG), plus engineering complexity in maintaining document-structure extraction. Failure modes include documents lacking reliable TOCs or sitemaps, retrieval proposing poor starting positions that mislead the induced sub-tree, and agent navigation errors accumulating over multiple steps, which may not justify the gains on small or weakly structured corpora.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.11370",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "On EntQABench (2.84M technical-documentation pages), improves answer accuracy by 6.8 to 11.4 points over the strongest baseline across three LLMs; highest accuracy among vanilla, graph-based, and agentic baselines on financial, scientific, and customer-support benchmarks.",
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      "source_url": "https://arxiv.org/abs/2610.11370",
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    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}