{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per 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": "2609.19524",
  "title": "A Unified Evaluation Framework for Trustworthy Large Language Models, Agentic AI, and Multimodal Systems",
  "summary": "This replaces ad hoc, system-specific evaluation scripts and single benchmark scores with a structured framework that normalizes output-level, trajectory-level, and cross-modal metrics into eight common trustworthiness dimensions with uncertainty estimates, evidence traces, and safety-critical overrides. The cost is engineering effort: instrumenting metrics across dimensions, defining band mappings, maintaining a meta-evaluation layer for validity and reproducibility, and aligning results to governance and regulatory mappings, all of which add evaluation pipeline complexity rather than GPU or latency overhead. What can fail is that the band normalization is a design choice with no validated ground truth, so scores can appear comparable while masking calibration errors, and the authors state that empirical validation across deployment contexts remains future work.",
  "rationale": "The framework is implementable with constrained infrastructure since it is a methodology rather than a model, but its band mappings and safety-override thresholds are unvalidated, so a small team should pilot it on one workload rather than adopt it as an oversight basis.",
  "technique": "Unified trustworthiness evaluation framework with metric band mapping",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19524",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This replaces ad hoc, system-specific evaluation scripts and single benchmark scores with a structured framework that normalizes output-level, trajectory-level, and cross-modal metrics into eight common trustworthiness dimensions with uncertainty estimates, evidence traces, and safety-critical overrides. The cost is engineering effort: instrumenting metrics across dimensions, defining band mappings, maintaining a meta-evaluation layer for validity and reproducibility, and aligning results to governance and regulatory mappings, all of which add evaluation pipeline complexity rather than GPU or latency overhead. What can fail is that the band normalization is a design choice with no validated ground truth, so scores can appear comparable while masking calibration errors, and the authors state that empirical validation across deployment contexts remains future work.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19524",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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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,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "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": ""
}