{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2610.13172",
  "title": "RoleWorld: Knowledge Transfer in World Modeling with Functional Role Abstractions",
  "summary": "It replaces environment-specific symbolic rules or programs with reusable functional role prototypes plus environment-specific parameter estimates, keeping transition rules fixed and refining role assignments online without LLM parameter updates. Cost is a teacher-LLM extraction phase per source environment, additional actor-LLM calls for skill-guided decision making and uncertainty-driven hypothesis testing, plus the engineering burden of maintaining executable rules, role assignments, and parameter estimators. It can fail when the teacher extracts incorrect or non-transferable role abstractions, when online parameter estimation converges too slowly or wrongly, or when the target environment's dynamics do not actually share the assumed interaction structure.",
  "rationale": "The approach is API-compatible and requires no training, but its benefit is validated only on a single benchmark and depends on workload-specific transfer structure, so small-scale validation is warranted before production adoption.",
  "technique": "Functional role abstractions for transferable symbolic world models",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13172",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 338,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "dimension": "compatibility",
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      "dimension": "reproducibility",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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