{
  "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": "2610.30933",
  "title": "ReflectPO: Reflection-Guided Policy Optimization for Long-Horizon LLM Agents",
  "summary": "ReflectPO replaces standard RL fine-tuning of tool-use agents (e.g., trajectory-level reward methods) with reflection-guided sampling and tree-structured action-level credit assignment using parent-child reward differences and recovery gains. It costs a full RL training pipeline with tree-organized trajectory sampling, environment branching, and reward instrumentation, which is infeasible on a single 24 GB GPU or API-only access without dedicated training infrastructure and budget. It can fail if reward signals are sparse or mis-specified, if hint construction from branch differences does not generalize to the reader's domain, or if the improved error-recovery behavior does not transfer to different tool environments.",
  "rationale": "The method requires reinforcement-learning training of the underlying model with tree-structured sampling infrastructure, which exceeds the reader's constrained infrastructure and API-based access, so it should be monitored rather than adopted.",
  "technique": "Reflection-guided RL for agent error recovery",
  "family": "recuperacao_de_falha",
  "recommendation": "observar",
  "published": "2026-10-22",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30933",
  "independent_implementations": 4,
  "total_implementations": 4,
  "stars": 152,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ReflectPO replaces standard RL fine-tuning of tool-use agents (e.g., trajectory-level reward methods) with reflection-guided sampling and tree-structured action-level credit assignment using parent-child reward differences and recovery gains. It costs a full RL training pipeline with tree-organized trajectory sampling, environment branching, and reward instrumentation, which is infeasible on a single 24 GB GPU or API-only access without dedicated training infrastructure and budget. It can fail if reward signals are sparse or mis-specified, if hint construction from branch differences does not generalize to the reader's domain, or if the improved error-recovery behavior does not transfer to different tool environments.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30933",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Outperforms multiple existing baselines in most domains and achieves best average performance on two long-horizon tool-use benchmarks; no multiplicative factor is reported in the abstract.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30933",
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      "source_excerpt": ""
    }
  ],
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      "finding": "",
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "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_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}