{
  "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": "2610.11171",
  "title": "VAMR: Multi-Question Agentic Reasoning for Efficient Long-Form Video Understanding",
  "summary": "VAMR replaces isolated per-question tool-use trajectories for long-form video understanding with one shared trajectory that jointly gathers evidence and maintains layered multi-question memory, trained via SFT plus question-horizon policy optimization. The cost is a training pipeline (supervised fine-tuning plus RL with a question-level critic) and a more complex orchestration and memory design, which exceeds a simple API-call setup unless a pretrained checkpoint is available. It can fail when one difficult question extends the shared trajectory for all questions, when errors in the shared video story propagate across answers, and when the trained policy does not transfer to video domains or tools outside its training distribution.",
  "rationale": "The efficiency and accuracy gains are relevant to constrained infrastructure, but realizing them requires either a released checkpoint or an SFT-plus-RL training budget, so it warrants a small-scale pilot rather than immediate adoption.",
  "technique": "Shared multi-question agentic video reasoning (VAMR)",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11171",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": 0,
  "claims": [
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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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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
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  "equations_status": "not_fetched",
  "core_kind": "",
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}