{
  "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": "2609.25373",
  "title": "Extending FunctionGemma for Practical On-Device Mobile Function Calling",
  "summary": "The method replaces prompting a large general-purpose LLM (or using a narrow specialist) for mobile function calling with supervised fine-tuning of a 270M model on a schema-validated synthetic dataset of ~9,500 conversations covering fifteen Android control categories. Cost is modest: SFT of a 270M model with completion-only loss fits on a single 24 GB GPU, and the released dataset and pipeline reduce data-engineering effort; the trade-off is the 8.0-point accuracy loss on Google's benchmark when combining both datasets. It can fail on user requests outside the fifteen trained categories, on schema drift between training and deployment, and because a fully synthetic dataset may not represent real user phrasing, so validation on actual target workflows is required.",
  "rationale": "A 270M fine-tuned model is cheap to train and serve on constrained infrastructure, the artifacts are released, and the accuracy gain over the base model is large enough to justify adoption for mobile or embedded function-calling use cases.",
  "technique": "Fine-tuned 270M specialist for on-device function calling",
  "family": "uso_de_ferramenta",
  "recommendation": "adotar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.25373",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "dimension": "operations",
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      "dimension": "compatibility",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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