{
  "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": "2610.21266",
  "title": "TRACER: A Multimodal Harness for Patient Care in Hospitals",
  "summary": "TRACER replaces task-specific model training on protected health data with a training-free pipeline that builds minute-level action records from video, aggregates them with clinical records into hourly reports, and lets an LLM agent query this hierarchy through tools. The cost is a multi-stage perception and summarization stack plus repeated vision and LLM inference over long video (hundreds of hours per patient), which on a single 24 GB GPU or API budget means substantial preprocessing latency and inference spend. It can fail through error propagation from imperfect action detection into downstream reports and answers, dependence on backbone quality, and evaluation limited to two ICU patients for QA, so accuracy may not transfer to other wards or hospitals.",
  "rationale": "The approach is production-relevant only for teams with continuous hospital video and clinical-record access under privacy constraints, and its small-scale clinical evaluation and heavy per-patient inference cost make it a technique to track rather than adopt or test in general practice.",
  "technique": "Tool-equipped LLM agent over video-clinical hierarchy",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21266",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "TRACER replaces task-specific model training on protected health data with a training-free pipeline that builds minute-level action records from video, aggregates them with clinical records into hourly reports, and lets an LLM agent query this hierarchy through tools. The cost is a multi-stage perception and summarization stack plus repeated vision and LLM inference over long video (hundreds of hours per patient), which on a single 24 GB GPU or API budget means substantial preprocessing latency and inference spend. It can fail through error propagation from imperfect action detection into downstream reports and answers, dependence on backbone quality, and evaluation limited to two ICU patients for QA, so accuracy may not transfer to other wards or hospitals.",
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    {
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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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",
  "core_kind": "",
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}