{
  "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": "2610.29751",
  "title": "Bridging Foundational and Agentic Capabilities of LLMs",
  "summary": "The method replaces end-to-end agent benchmark scoring with a dual-profile framework that compares foundational capability benchmarks against operation-level interaction trajectories to locate execution failures. It costs the effort of collecting and annotating interaction trajectories plus fitting predictive mappings, which is feasible on a limited budget but adds evaluation engineering overhead. It can fail if the τ²-Bench-derived operation taxonomy does not transfer to the reader's production task distribution, or if foundational benchmarks mismeasure the capabilities actually exercised by the deployment.",
  "rationale": "The framework requires no GPU-heavy training and could improve failure diagnosis in agent deployments, but its predictive value must be validated on the reader's specific tasks and model services before adoption.",
  "technique": "Operation-level agentic capability measurement",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-21",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.29751",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
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  ],
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      "dimension": "compatibility",
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      "dimension": "security",
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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",
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}