{
  "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": "2610.01166",
  "title": "CineMR: Tool-Integrated Vision-Language Reasoning for Quantitative Cardiac MRI Assessment",
  "summary": "CineMR replaces direct vision-language prediction of cardiac measurements with a VLM that calls segmentation, volumetry, morphometry, and wall-motion tools and reasons over their outputs, trained via SFT on tool traces plus GRPO with tool-use rewards. Adoption requires implementing and validating the domain-specific analysis tools and running a two-stage SFT-plus-GRPO training pipeline, which is feasible only with LoRA-scale tuning on a single 24 GB GPU and is specific to the cardiac MRI domain. It can fail through tool invocation errors in the residual 0.2% of cases, compounding errors from the underlying analysis tools, distribution shift across scanner cohorts, and the absolute pass@1 of 35.9% remaining too low for autonomous clinical use.",
  "rationale": "The tool-integrated reasoning pattern is transferable, but the contribution is tightly coupled to a regulated medical-imaging domain, a bespoke tool stack, and a reinforcement-learning training stage that exceed what a constrained-infrastructure team can adopt directly without a cardiac MRI use case.",
  "technique": "Tool-augmented VLM for quantitative cardiac MRI",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01166",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "CineMR replaces direct vision-language prediction of cardiac measurements with a VLM that calls segmentation, volumetry, morphometry, and wall-motion tools and reasons over their outputs, trained via SFT on tool traces plus GRPO with tool-use rewards. Adoption requires implementing and validating the domain-specific analysis tools and running a two-stage SFT-plus-GRPO training pipeline, which is feasible only with LoRA-scale tuning on a single 24 GB GPU and is specific to the cardiac MRI domain. It can fail through tool invocation errors in the residual 0.2% of cases, compounding errors from the underlying analysis tools, distribution shift across scanner cohorts, and the absolute pass@1 of 35.9% remaining too low for autonomous clinical use.",
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      "source_url": "https://arxiv.org/abs/2610.01166",
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      "statement": "35.9% pass@1 on a multi-cohort cine CMR benchmark versus 1.5% for the Qwen3-VL-8B backbone; correct tool invocation reaches 99.8% after GRPO, and removing tools drops pass@1 to 27.9%.",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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      "dimension": "security",
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    {
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}