{
  "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": "2610.20388",
  "title": "From Relevance to Validity: Zero-Shot Tool Selection via Functional and Contextual Validation",
  "summary": "The method replaces pure semantic relevance ranking for tool selection with a progressive pipeline that adds endpoint-grounded functional compatibility checks and task-conditioned contextual validation on top of retrieval. The cost is additional LLM calls per candidate tool for the functional and contextual validation stages, increasing selection latency and API spend, plus engineering effort to ground tool endpoints. It can fail when tool endpoint specifications are incomplete or inaccurate, when the validation LLM misjudges functional compatibility, and in domains where the contextual evidence it relies on does not transfer.",
  "rationale": "The framework runs on top of existing retrieval and LLM APIs, so it fits constrained infrastructure, but its unquantified ranking gains must be validated against the reader's actual tool inventory before production adoption.",
  "technique": "Progressive validation for zero-shot tool selection",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20388",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.20388",
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      "source_excerpt": ""
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      "source_url": "https://arxiv.org/abs/2610.20388",
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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_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}