{
  "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": "2610.20780",
  "title": "ARIA: Runtime Inspection and Amendment for Multi-Agent Systems",
  "summary": "Replaces post-hoc diagnosis of completed multi-agent failures and repeated re-execution search with a compact learned model that intervenes on ongoing runs, trained on 3,953 execution-verified repair pairs derived from over 24,000 trajectories. Costs include assembling or reusing verified repair data, fine-tuning and hosting an additional inspector model, per-step inspection latency, and integration work requiring the MAS to expose trajectories and support continuation replacement. It can fail on failure modes unlike the training repairs, misjudge when to intervene and corrupt otherwise healthy runs, and its claimed generalization to unseen frameworks and host models may not hold for the reader's specific stack.",
  "rationale": "The approach is feasible on constrained infrastructure since it trains only a compact model, but its value depends on whether the reader's multi-agent failures resemble the verified-repair distribution, so it warrants a small-scale pilot rather than direct adoption.",
  "technique": "Learned runtime inspection and repair for multi-agent executions",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20780",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces post-hoc diagnosis of completed multi-agent failures and repeated re-execution search with a compact learned model that intervenes on ongoing runs, trained on 3,953 execution-verified repair pairs derived from over 24,000 trajectories. Costs include assembling or reusing verified repair data, fine-tuning and hosting an additional inspector model, per-step inspection latency, and integration work requiring the MAS to expose trajectories and support continuation replacement. It can fail on failure modes unlike the training repairs, misjudge when to intervene and corrupt otherwise healthy runs, and its claimed generalization to unseen frameworks and host models may not hold for the reader's specific stack.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20780",
      "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",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "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_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}