{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2609.09153",
  "title": "Procedural Graphs: Self-Evolving Execution Structures for LLM Agents",
  "summary": "Replaces unconstrained next-action generation over an accumulating trajectory with an evolving (procedure, relation, procedure) graph that a guidance model turns into step-level hints biasing, but not dictating, the agent's next action. Costs an extra guidance-model call per decision step plus LLM-refiner passes that contrast failed and successful trajectories to edit the graph against a held-out validation set, adding orchestration complexity and API spend. Can fail if validation sets are small or unrepresentative, letting the refiner overfit graph edits, or if localization of the active node is wrong on long trajectories, injecting misleading guidance; the abstract reports only qualitative 'consistent gains' with no quantified improvement, so the benefit is unverified for any specific workload.",
  "rationale": "The method is API-compatible and needs no training or cluster, but the abstract provides no quantified gains, so adoption should follow a workload-specific A/B evaluation against the reader's existing memory or prompting baseline.",
  "technique": "Self-evolving procedural graph for agent guidance",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.09153",
  "independent_implementations": 14,
  "total_implementations": 14,
  "stars": 17,
  "citations": 0,
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    {
      "claim_id": "claim-01",
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      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.09153",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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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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      "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",
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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_formula",
  "core_kind": "system",
  "equations_fetched_at": "2026-09-28",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}