{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful 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": "2609.15726",
  "title": "Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands",
  "summary": "Bench2Dex replaces ad hoc, per-hand experimental setups in robotics research with a unified simulation benchmark spanning 12 dexterous hands, 26 bimanual tasks, and approximately 1.3K teleoperated demonstrations. Adoption requires a robot-learning simulation stack and training of manipulation policies (ACT, Diffusion Policy, pi0.5, GR00T N1.5), which is outside language-model production workloads and may exceed a single 24 GB GPU depending on the policy. Simulated tactile observations do not match physical sensor output, so results can fail to transfer to real hardware.",
  "rationale": "This is a robotics simulation benchmark with no application to the reader's production ML practice of deploying or serving models under constrained infrastructure.",
  "technique": "Visuo-tactile bimanual dexterous manipulation benchmark",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.15726",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 68,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Bench2Dex replaces ad hoc, per-hand experimental setups in robotics research with a unified simulation benchmark spanning 12 dexterous hands, 26 bimanual tasks, and approximately 1.3K teleoperated demonstrations. Adoption requires a robot-learning simulation stack and training of manipulation policies (ACT, Diffusion Policy, pi0.5, GR00T N1.5), which is outside language-model production workloads and may exceed a single 24 GB GPU depending on the policy. Simulated tactile observations do not match physical sensor output, so results can fail to transfer to real hardware.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.15726",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
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      "finding": "",
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_formula",
  "core_kind": "system",
  "equations_fetched_at": "2026-10-08",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}