{
  "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": "2609.27273",
  "title": "CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments",
  "summary": "CAVEAT-Harness replaces naive prompting of computer-use agents in incentive-misaligned environments with a scaffold that counters three diagnosed failure points: distorted user priorities, prematurely narrowed candidate sets, and early commitment before evidence resolution. The cost is additional orchestration and inference overhead per episode, plus benchmark-specific tuning, with optional targeted post-training if a smaller open model is used. It can fail when steering mechanisms differ from the eight in the taxonomy, when the harness itself is steered, or when residual failures persist even after the 55% relative improvement, since absolute robustness remains well below control performance.",
  "rationale": "The reader runs agents through APIs or a single GPU and can adopt a prompt/harness-level intervention at low cost, but the 55% relative gain is measured on one marketplace benchmark and must be validated on the reader's actual environments and steering threats before production reliance.",
  "technique": "Harness against environmental steering of web agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27273",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 2,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "baseline": "",
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    {
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      "statement": "CAVEAT-Harness raises user-optimal purchasing by 55.0% relative to the baseline agent under steering mechanisms (from a 17.3% steered baseline versus 78.6% in matched controls).",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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    },
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      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
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  "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": "",
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  "equations_source_sha256": "",
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}