{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "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": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "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": "2609.29892",
  "title": "Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents",
  "summary": "The method replaces ad-hoc prompt engineering and static fine-tuning of planner agents with a full closed-loop pipeline combining human-gated agentic data generation, supervised cold start, and hybrid-environment online reinforcement learning with competence-aware reward shaping. The cost is substantial: real-device interaction infrastructure, a multi-agent data flywheel, online RL training capacity, and co-evolving runtime harness engineering, none of which fit a single 24 GB GPU or API-only budget. After adoption, reward engineering can overfit to cost-reduction signals at the expense of task success, failure-trace feedback loops can propagate harness bugs into model updates, and gains may not transfer off MobilePA-Bench.",
  "rationale": "The framework requires online RL, real-device interaction farms, and a multi-agent data pipeline that exceed constrained infrastructure, and the abstract reports only unquantified relative improvements, so the reader should monitor rather than adopt.",
  "technique": "Closed-loop agentic RL training framework (CARE reward engineering)",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29892",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 110,
  "citations": 0,
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    {
      "claim_id": "claim-01",
      "statement": "The method replaces ad-hoc prompt engineering and static fine-tuning of planner agents with a full closed-loop pipeline combining human-gated agentic data generation, supervised cold start, and hybrid-environment online reinforcement learning with competence-aware reward shaping. The cost is substantial: real-device interaction infrastructure, a multi-agent data flywheel, online RL training capacity, and co-evolving runtime harness engineering, none of which fit a single 24 GB GPU or API-only budget. After adoption, reward engineering can overfit to cost-reduction signals at the expense of task success, failure-trace feedback loops can propagate harness bugs into model updates, and gains may not transfer off MobilePA-Bench.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.29892",
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      "source_excerpt": ""
    }
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  "exposure_map": [
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      "dimension": "operations",
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      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}