{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task budget.",
    "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": "Correct completed actions, invalid calls, retries, latency, and cost per completed 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": "2609.27249",
  "title": "From PyTorch to the NPU: LLM-Agent-Driven Model Conversion Across Heterogeneous Inference Runtimes",
  "summary": "The method replaces manual, engineer-driven model porting (PyTorch to OpenVINO, RKNN, TensorRT, or ONNX Runtime) with an LLM agent that executes staged conversion, operator repair, and precision verification using injected runtime-specific knowledge and a layout-adaptation layer. The cost is agent orchestration overhead, dependence on runtime-specific skills and auxiliary scripts that must be authored and maintained per backend, and the paper's own characterization of agent deviation behavior even under structured knowledge injection. Failures include agent divergence during operator-compatibility repair, FP16 precision mismatches caught only by staged verification, and toolchain brittleness when target runtime versions change.",
  "rationale": "The technique targets heterogeneous edge hardware (RK3588, Qualcomm NPU, x86/NPU) that the reader's single-GPU, API-based infrastructure does not include, and the paper provides engineering practice experience rather than validated quantitative gains.",
  "technique": "LLM-agent-driven multi-runtime model conversion",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27249",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.27249",
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      "source_excerpt": ""
    }
  ],
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "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": ""
}