{
  "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": "2609.18304",
  "title": "Rollback the World, Keep the Reflection: Rollback-Induced Reflection for Long-Horizon LLM Agents",
  "summary": "RIR replaces ad-hoc failure handling that either patches the context without restoring environment state or reverts state while discarding everything learned, by jointly choosing when to intervene, which prior state to resume from, and what distilled reflection to carry forward. It costs an additional reflection/distillation pass (extra LLM calls per recovery event), a checkpointable environment with state snapshots, and memory for structured reflection, all of which are feasible on API-based agents but require engineering for rollback boundaries. It can fail when the environment is not checkpointable or reversible (e.g., irreversible API side effects), when rollback-boundary selection is miscalibrated and discards valid progress, or when distilled reflections preserve incorrect lessons and bias subsequent decisions.",
  "rationale": "The mechanism is implementable on top of existing API-based agent loops without special hardware, but its benefit depends on having a checkpointable environment and workload-specific failure patterns, so it warrants a targeted pilot rather than direct adoption.",
  "technique": "Rollback with distilled reflection memory for agent recovery",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18304",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RIR replaces ad-hoc failure handling that either patches the context without restoring environment state or reverts state while discarding everything learned, by jointly choosing when to intervene, which prior state to resume from, and what distilled reflection to carry forward. It costs an additional reflection/distillation pass (extra LLM calls per recovery event), a checkpointable environment with state snapshots, and memory for structured reflection, all of which are feasible on API-based agents but require engineering for rollback boundaries. It can fail when the environment is not checkpointable or reversible (e.g., irreversible API side effects), when rollback-boundary selection is miscalibrated and discards valid progress, or when distilled reflections preserve incorrect lessons and bias subsequent decisions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18304",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Consistently improves task performance across multiple LLM backbones on three long-horizon benchmarks; no numeric magnitude is given in the abstract.",
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      "source_url": "https://arxiv.org/abs/2609.18304",
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    }
  ],
  "exposure_map": [
    {
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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}