{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per 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": "2610.30051",
  "title": "From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation Tasks",
  "summary": "The approach replaces task-specific manipulation training or pretrained skill policies with direct closed-loop control by a frontier multimodal model issuing end-effector poses and gripper commands. It costs per-episode API inference that scales with reasoning effort, requires a physical or simulated robot stack, and adds integration complexity for vision, proprioception, and tool-call handling. With a best success rate of 44% and weakness on precise spatial and multi-step tasks, it fails on reliability-critical deployments and offers no guaranteed recovery when a subtask misfires.",
  "rationale": "The paper is a benchmark evaluation showing current frontier models are not reliable enough for production manipulation, so it informs technology watch rather than adoption.",
  "technique": "Zero-shot VLM-based robot manipulation benchmarking",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-22",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30051",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
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      "source_url": "https://arxiv.org/abs/2610.30051",
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    }
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      "dimension": "operations",
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    },
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      "dimension": "compatibility",
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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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}