{
  "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": "2609.30199",
  "title": "ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds",
  "summary": "ExplorationBench replaces ad hoc or recall-contaminated evaluations of agent exploration with two executable sandboxes (AlienCode and AlienLogic) whose novel rules permit exact answer verification and defeat memorization from pre-training data. Adoption costs engineering effort to integrate the custom tool-call schemas and sandbox environments, plus API or GPU compute for multi-trajectory runs, which can be significant given the reported high variance across trajectories. It can fail as a decision instrument because results vary substantially between runs and continued exploration can stall or reverse gains, so small-sample comparisons may mislead, and sandbox performance may not transfer to real production tasks.",
  "rationale": "This is an evaluation benchmark rather than a deployable capability, so it is only relevant if the reader needs to measure exploratory reasoning in agents, and even then its trajectory variance demands careful experimental design.",
  "technique": "Benchmark for agentic exploration in verifiable synthetic worlds",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.30199",
  "independent_implementations": 4,
  "total_implementations": 4,
  "stars": 11,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ExplorationBench replaces ad hoc or recall-contaminated evaluations of agent exploration with two executable sandboxes (AlienCode and AlienLogic) whose novel rules permit exact answer verification and defeat memorization from pre-training data. Adoption costs engineering effort to integrate the custom tool-call schemas and sandbox environments, plus API or GPU compute for multi-trajectory runs, which can be significant given the reported high variance across trajectories. It can fail as a decision instrument because results vary substantially between runs and continued exploration can stall or reverse gains, so small-sample comparisons may mislead, and sandbox performance may not transfer to real production tasks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.30199",
      "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",
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      "finding": "",
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      "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": ""
}