{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Tool errors or unavailable services cause your agent to abandon recoverable tasks.",
    "change": "Inject repeatable tool failures and change only the recovery policy.",
    "baseline": "Your current retry and fallback behavior under the same injected failures.",
    "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": "Recovered tasks, duplicate side effects, retries, time to recovery, and total cost.",
    "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": "2610.10462",
  "title": "FoldBack: Self-Correcting Masked Generative Policy for Long-Horizon Garment Folding",
  "summary": "FoldBack replaces open-loop long-horizon garment-folding policies that continue executing after a failed grasp, adding event-aligned grasp verification, rollback to a retryable pre-grasp state, and selective regeneration of failed trajectory segments without retraining or recovery demonstrations. The cost is additional inference-time machinery: grasp verification at each pick-and-place event, state restoration logic, and partial trajectory regeneration, which increases latency and system complexity and presumes reversible robot states. It can fail when errors are not physically reversible (deformed or entangled garments), when verification itself misjudges a grasp, and it is specific to a bimanual robotics setup with garment masks, requiring hardware and perception infrastructure outside a typical ML engineering stack.",
  "rationale": "The method targets physical robot garment folding, a domain requiring robotic hardware and perception pipelines that the constrained-infrastructure ML engineer does not operate, so its gains are not actionable for their production practice.",
  "technique": "Self-correcting masked trajectory policy with rollback",
  "family": "recuperacao_de_falha",
  "recommendation": "nao_aplica",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10462",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 215,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FoldBack replaces open-loop long-horizon garment-folding policies that continue executing after a failed grasp, adding event-aligned grasp verification, rollback to a retryable pre-grasp state, and selective regeneration of failed trajectory segments without retraining or recovery demonstrations. The cost is additional inference-time machinery: grasp verification at each pick-and-place event, state restoration logic, and partial trajectory regeneration, which increases latency and system complexity and presumes reversible robot states. It can fail when errors are not physically reversible (deformed or entangled garments), when verification itself misjudges a grasp, and it is specific to a bimanual robotics setup with garment masks, requiring hardware and perception infrastructure outside a typical ML engineering stack.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.10462",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "75.2% final folding success and 0.837 final-mask IoU versus 45.7% and 0.689 for the strongest prior baseline across 33 real garments",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.10462",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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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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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "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_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}