{
  "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": "2610.00557",
  "title": "No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents",
  "summary": "The method replaces single-environment evaluation of hierarchical red team agents with a controlled cross-environment comparison of homogeneous RL+RL and LLM+LLM architectures under one unified disruption metric. The cost is substantial infrastructure: two simulation environments (CybORG CAGE-4, Cyberwheel), expert autonomous defenders, RL training, and 18 configuration runs, none of which directly transfers to production LLM serving. Adoption of its conclusion without local validation can fail because the architecture ranking inverts with environment scale and reward density, so an agent architecture chosen on one benchmark may be the worst choice in a differently shaped deployment setting.",
  "rationale": "The paper is an evaluation study of autonomous cyber red team architectures whose results depend on specialized simulation environments and RL training that the reader cannot reproduce or deploy, so its practical value is limited to the meta-lesson that agent architecture choices must be validated against workload-specific failure modes rather than assumed universal.",
  "technique": "Cross-environment evaluation of RL and LLM red team hierarchies",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00557",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces single-environment evaluation of hierarchical red team agents with a controlled cross-environment comparison of homogeneous RL+RL and LLM+LLM architectures under one unified disruption metric. The cost is substantial infrastructure: two simulation environments (CybORG CAGE-4, Cyberwheel), expert autonomous defenders, RL training, and 18 configuration runs, none of which directly transfers to production LLM serving. Adoption of its conclusion without local validation can fail because the architecture ranking inverts with environment scale and reward density, so an agent architecture chosen on one benchmark may be the worst choice in a differently shaped deployment setting.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.00557",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "RL+RL achieves 78.5% disruption success versus 18.0% for the strongest LLM configuration in CAGE-4 and 81.0% versus 50.5% on the 100-host Cyberwheel network, while a pretrained cybersecurity LLM agent achieves 55.0% versus 0.0% for RL on the 1010-host Cyberwheel network.",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.00557",
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      "source_excerpt": ""
    }
  ],
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      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "source_page": null,
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    },
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      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "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": "",
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}