{
  "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": "2609.17320",
  "title": "Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems",
  "summary": "Emergence World replaces single-response model evaluation with continuous adversarial stress-testing of long-horizon multi-agent systems, injecting prompt injection, misinformation, and memory exposure through ordinary interaction surfaces after operational state accumulates. The cost is prohibitive for constrained infrastructure: the study required 16 days, eight parallel worlds, over 850,000 LLM calls, and roughly 50 billion tokens, plus substantial engineering of persistent memory, tools, and shared institutions. What can fail in adoption is that results from large homogeneous frontier-model worlds may not transfer to a small production deployment, and the paper offers findings rather than mitigations, so detected failure modes such as memory poisoning and delayed action on adversarial content remain unsolved.",
  "rationale": "The reader cannot replicate this scale of evaluation on one 24 GB GPU or a limited API budget, but the qualitative findings justify adding lightweight adversarial stress tests and persistent-memory sanitization to any production agent system.",
  "technique": "Adversarial stress-testing of persistent multi-agent systems",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17320",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Emergence World replaces single-response model evaluation with continuous adversarial stress-testing of long-horizon multi-agent systems, injecting prompt injection, misinformation, and memory exposure through ordinary interaction surfaces after operational state accumulates. The cost is prohibitive for constrained infrastructure: the study required 16 days, eight parallel worlds, over 850,000 LLM calls, and roughly 50 billion tokens, plus substantial engineering of persistent memory, tools, and shared institutions. What can fail in adoption is that results from large homogeneous frontier-model worlds may not transfer to a small production deployment, and the paper offers findings rather than mitigations, so detected failure modes such as memory poisoning and delayed action on adversarial content remain unsolved.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.17320",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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    },
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      "dimension": "compute",
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    {
      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "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_formula",
  "core_kind": "system",
  "equations_fetched_at": "2026-10-18",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}