{
  "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": "2609.19656",
  "title": "Self-Evolving Search Index",
  "summary": "Replaces manual, human-driven diagnosis and rewriting of index keys (e.g., chunk representations, keyword/summary keys) with an automated optimizer that detects retrieval failures, revises the responsible keys, validates revisions, and simulates additional queries. Costs include repeated LLM calls for diagnosis, key rewriting, validation, and query simulation, plus periodic re-indexing and added pipeline complexity, all feasible on a single GPU or via APIs but with ongoing per-document inference expense. It can fail if the query simulator generates unrepresentative queries that drift the index away from real workload needs, if validation is too weak to catch harmful key revisions, or if retrieval failures stem from the retriever or corpus rather than the index keys, in which case the optimization loop yields little gain.",
  "rationale": "The approach is implementable with API-level LLM calls and standard retrievers, but its benefit depends on whether the reader's retrieval failures are actually caused by index key quality, so it warrants a controlled small-scale test against their own query logs rather than immediate adoption.",
  "technique": "Self-evolving retrieval index optimization",
  "family": "recuperacao_e_rag",
  "recommendation": "testar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19656",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces manual, human-driven diagnosis and rewriting of index keys (e.g., chunk representations, keyword/summary keys) with an automated optimizer that detects retrieval failures, revises the responsible keys, validates revisions, and simulates additional queries. Costs include repeated LLM calls for diagnosis, key rewriting, validation, and query simulation, plus periodic re-indexing and added pipeline complexity, all feasible on a single GPU or via APIs but with ongoing per-document inference expense. It can fail if the query simulator generates unrepresentative queries that drift the index away from real workload needs, if validation is too weak to catch harmful key revisions, or if retrieval failures stem from the retriever or corpus rather than the index keys, in which case the optimization loop yields little gain.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19656",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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    },
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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",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}