{
  "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": "2610.02089",
  "title": "HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution",
  "summary": "This paper introduces a benchmark and 3.1k-demonstration dataset for humanoid tool use, replacing ad-hoc evaluation of tool selection and execution with 18 standardized tasks; it is not a method that replaces any component of an LLM production stack. Adoption requires a Unitree G1 humanoid or a robotics simulation environment, neither of which exists in the reader's constrained infrastructure, and the stated costs are data collection and robot hardware, not GPU budget. The reported failure modes (reduced selection accuracy on unseen tools and execution continuing under unrelated instructions) concern robotics foundation models such as GR00T N1.7 and do not transfer to text-only LLM serving or agent work.",
  "rationale": "The work targets humanoid robotics evaluation requiring dedicated hardware and simulation, which is outside the reader's LLM production scope and infrastructure.",
  "technique": "Humanoid robot tool-use benchmark (HumanoidToolBench)",
  "family": "uso_de_ferramenta",
  "recommendation": "nao_aplica",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.02089",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 9,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}