{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2610.06650",
  "title": "Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents",
  "summary": "It replaces direct tool calling, where large graph query results are appended to the model context, with a harness that batches graph calls, stores results in persistent Python state, and processes selected evidence through sub-calls, alongside a released dataset of 10,235 solving traces on a frozen Wikidata snapshot. The cost is additional orchestration complexity, sub-call latency, and dependence on a frozen snapshot plus a SPARQL-like interface rather than a general retrieval stack. It can fail outside the evaluated setting: gains are reported on only 100 questions, uniqueness-constrained synthetic multi-hop questions may not match real query distributions, and the traces and harness are specific to Wikidata rather than arbitrary enterprise knowledge bases.",
  "rationale": "The harness runs with an open-weight model on a single 24 GB GPU and shows a measurable accuracy gain, but the small evaluation set and Wikidata-specific construction require validation on the reader's own workload before production adoption.",
  "technique": "Recursive LLM harness for graph search agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06650",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
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      "claim_id": "claim-01",
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    {
      "claim_id": "claim-02",
      "statement": "On 100 questions, the RLM harness raises accuracy over direct tool calling from 49 to 61 for gpt-6-luna and from 60 to 74 for Qwen3.8-27B served on a single GPU; multi-hop accuracy roughly doubles for gpt-6-luna.",
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  ],
  "exposure_map": [
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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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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  "risks": [],
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  "open_questions": [],
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  "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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}