{
  "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": "2610.00972",
  "title": "VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks",
  "summary": "VeriHarness replaces single-rollout generation (and naive self-consistency voting) with a verifier agent built from the same LLM, using a workspace with evidence tools to resolve disagreements between sampled rollouts, challenge consensus claims, and revise the final artifact. It costs multiple rollouts plus verifier agent passes per task, multiplying inference spend roughly proportional to the sampling budget, and it requires tool access to environment evidence, so it fits API-based setups but adds orchestration complexity. It can fail when no sampled rollout contains the correct claims, when the verifier's evidence tools are unavailable or misleading, or when the same base model's biases lead it to endorse shared errors that consensus conceals.",
  "rationale": "The 6-point quality gain over single rollouts is achievable with third-party APIs alone, but the multi-sample plus verifier cost multiplier and the need for evidence-tool access mean the reader should validate the cost-benefit ratio on their own workload before production adoption.",
  "technique": "Agentic multi-rollout verification and revision",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00972",
  "independent_implementations": 4,
  "total_implementations": 5,
  "stars": 139,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "VeriHarness replaces single-rollout generation (and naive self-consistency voting) with a verifier agent built from the same LLM, using a workspace with evidence tools to resolve disagreements between sampled rollouts, challenge consensus claims, and revise the final artifact. It costs multiple rollouts plus verifier agent passes per task, multiplying inference spend roughly proportional to the sampling budget, and it requires tool access to environment evidence, so it fits API-based setups but adds orchestration complexity. It can fail when no sampled rollout contains the correct claims, when the verifier's evidence tools are unavailable or misleading, or when the same base model's biases lead it to endorse shared errors that consensus conceals.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.00972",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Evidence-backed revision improves average performance by 6.2 points over a single rollout with Gemini 3.5 Flash and 6.4 points with Claude Opus 4.8 across five long-horizon workspace benchmarks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.00972",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": ""
}