{
  "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": "2610.06100",
  "title": "From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation",
  "summary": "Trace2Env replaces reconstructing an executable environment with a learning-free pipeline that compiles historical interaction traces into a worldbook (schemas, grounded evidence, induced behavioral rules) consulted by an LLM world-model agent with persistent episodic state. The cost is additional inference per simulated step (worldbook retrieval plus state tracking), worldbook construction effort, and dependence on trace coverage rather than trained weights. It can fail on actions or states absent from the traces, where the model must hallucinate dynamics, and accumulated state-tracking errors can degrade long-horizon fidelity in ways the paper's validity metric only partially captures.",
  "rationale": "A constrained team can implement this with API access and existing traces, but its value depends entirely on whether available traces cover the action space of the target environment, so workload-specific validation is required before production reliance.",
  "technique": "Trace-derived LLM world models for environment simulation",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06100",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 226,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Trace2Env replaces reconstructing an executable environment with a learning-free pipeline that compiles historical interaction traces into a worldbook (schemas, grounded evidence, induced behavioral rules) consulted by an LLM world-model agent with persistent episodic state. The cost is additional inference per simulated step (worldbook retrieval plus state tracking), worldbook construction effort, and dependence on trace coverage rather than trained weights. It can fail on actions or states absent from the traces, where the model must hallucinate dynamics, and accumulated state-tracking errors can degrade long-horizon fidelity in ways the paper's validity metric only partially captures.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06100",
      "source_page": null,
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    },
    {
      "claim_id": "claim-02",
      "statement": "Across nine environments, Trace2Env improves next-observation fidelity and long-horizon interaction consistency over conventional prompt-based LWMs, and task-agent actions generated against it remain valid more often when replayed in the real environment; no multiplicative factor is reported.",
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      "source_url": "https://arxiv.org/abs/2610.06100",
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      "source_excerpt": ""
    }
  ],
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "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": "",
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}