{
  "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": "2610.13296",
  "title": "PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real",
  "summary": "PRIME replaces plain chunk-based corpus indexing with an index-side pipeline: LLM-driven semantic restructuring of documents plus deterministic metadata extraction and hierarchical section retrieval. It costs LLM calls at index time, added indexing complexity, and a quality-fidelity trade-off from restructuring, though it reduces embedding compute and vector count by large factors. It can degrade retrieval in certain corpus regimes (three qualitative failure/success regimes are reported), and its gains are neutralized when a strong reranker is used downstream.",
  "rationale": "The embedding-cost reduction is compelling for a single-GPU/API budget, but the reported quality regressions in some regimes and the null effect under reranking require validation on the reader's own corpus before adoption.",
  "technique": "Index-side semantic restructuring with metadata-hierarchical retrieval",
  "family": "recuperacao_e_rag",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13296",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PRIME replaces plain chunk-based corpus indexing with an index-side pipeline: LLM-driven semantic restructuring of documents plus deterministic metadata extraction and hierarchical section retrieval. It costs LLM calls at index time, added indexing complexity, and a quality-fidelity trade-off from restructuring, though it reduces embedding compute and vector count by large factors. It can degrade retrieval in certain corpus regimes (three qualitative failure/success regimes are reported), and its gains are neutralized when a strong reranker is used downstream.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.13296",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Deterministic metadata synthesis matches structure-aware chunking quality while cutting embedding computation by 45.8x and index vector count by 22.4x; Semantic Restructuring adds 13.7% Answer-F1 in full deployment.",
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      "source_url": "https://arxiv.org/abs/2610.13296",
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    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "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_page": null,
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    },
    {
      "dimension": "reproducibility",
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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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}