{
  "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": "2610.21088",
  "title": "Probe-and-Resume: When and How to Evaluate Agent Execution",
  "summary": "It replaces always-on MCTS-style critics that score intermediate agent states with a critic invoked only when the agent claims completion, probing a forked copy of the environment with read/write/execute access. The cost is extra critic rollouts on a forked environment plus fork infrastructure for the terminal sandbox; the authors report roughly half the cost of the Codex CLI baseline, but forking requires a reproducible, snapshotable environment. It can fail when the environment cannot be safely forked, when hidden state escapes even destructive probing, or when the critic incorrectly rejects a genuinely complete trajectory and wastes resume budget.",
  "rationale": "The gain is demonstrated on a single terminal benchmark and depends on forkable sandbox infrastructure, so a small team should validate it on their own agent workload before production adoption.",
  "technique": "Probe-and-Resume: fork-based critic at claimed-final states",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21088",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces always-on MCTS-style critics that score intermediate agent states with a critic invoked only when the agent claims completion, probing a forked copy of the environment with read/write/execute access. The cost is extra critic rollouts on a forked environment plus fork infrastructure for the terminal sandbox; the authors report roughly half the cost of the Codex CLI baseline, but forking requires a reproducible, snapshotable environment. It can fail when the environment cannot be safely forked, when hidden state escapes even destructive probing, or when the critic incorrectly rejects a genuinely complete trajectory and wastes resume budget.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21088",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "On Terminal-Bench 2.1, raises task success by 8.4 percentage points over MCTS with conventional critics, approaching the Codex CLI agent's success rate with the same underlying model at less than half its cost.",
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      "source_url": "https://arxiv.org/abs/2610.21088",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
      "source_url": "",
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}