{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task budget.",
    "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": "Correct completed actions, invalid calls, retries, latency, and cost per completed task.",
    "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": "2609.15779",
  "title": "EvoOntology: A Self-Evolving Ontology Layer for Data Agents",
  "summary": "EvoOntology replaces two prior strategies: letting agents directly explore raw heterogeneous data (schemas, files, databases) through generic tools, and injecting manually built semantic layers into prompts; it substitutes an automatically built, self-evolving ontology served as an MCP server with schema, content, and tool layers. The cost is an additional builder agent, an evolution loop with attribution-guided typed edits and backbone-conditional paired evaluation before edits are accepted, plus ongoing LLM calls for construction, evaluation, and ontology maintenance. It can fail when the builder induces incorrect ontology entries that misguide queries, when edit acceptance is too permissive or too strict for a given backbone, or when the ontology drifts from changing data sources and silently degrades agent answers.",
  "rationale": "The approach is feasible on a single-GPU or API-only setup since all components are LLM-driven agents, but its benefit depends on the reader's specific data sources and backbone, so it merits a workload-specific pilot rather than direct production adoption.",
  "technique": "Self-evolving ontology MCP layer for data agents",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-09-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.15779",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 543,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "EvoOntology consistently outperforms strong baselines and existing semantic-layer approaches on three data-agent benchmarks with four LLM backbones; no numeric improvement is stated in the abstract.",
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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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      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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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_formula",
  "core_kind": "none",
  "equations_fetched_at": "2026-10-09",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}