{
  "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": "2610.10786",
  "title": "Plan-and-Patch: Diffusion Language Models for Agentic Planning",
  "summary": "The method replaces full autoregressive plan regeneration, in which a failed step forces the entire plan to be rewritten, with a diffusion language model that regenerates only the masked, invalidated region while holding the surrounding plan steps fixed. The cost is adopting a non-standard diffusion LLM stack (e.g., DreamReasoner-8B) with its parallel-unmasking inference path, plus implementing plan parsing and region-selection logic to identify what to patch. Failure modes include incorrect localization of the region needing repair, degradation when a failure invalidates most of the plan rather than a contiguous segment, and dependence on a dLLM ecosystem that is less mature and less supported than AR serving infrastructure.",
  "rationale": "An 8B diffusion planner fits a 24 GB GPU and the repair-success gain is substantial, but the dLLM tooling and region-selection machinery require workload-specific validation before production adoption.",
  "technique": "Diffusion-LM plan generation with masked-region repair",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10786",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces full autoregressive plan regeneration, in which a failed step forces the entire plan to be rewritten, with a diffusion language model that regenerates only the masked, invalidated region while holding the surrounding plan steps fixed. The cost is adopting a non-standard diffusion LLM stack (e.g., DreamReasoner-8B) with its parallel-unmasking inference path, plus implementing plan parsing and region-selection logic to identify what to patch. Failure modes include incorrect localization of the region needing repair, degradation when a failure invalidates most of the plan rather than a contiguous segment, and dependence on a dLLM ecosystem that is less mature and less supported than AR serving infrastructure.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.10786",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "On Natural Plan without task-specific training, the diffusion planner (DreamReasoner-8B) achieves 53.7% plan-repair success versus 27.0% for the AR planner (Qwen3-8B), nearly twice the rate; it also reduces mean plan-generation latency by 39-46% after task-specific training on ALFWorld and TextCraft.",
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    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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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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}