{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2609.17695",
  "title": "GraphEcho: Structural Redundancy and Evidence Provenance in LLM Graph Agents",
  "summary": "GraphEcho is an evaluation benchmark plus a provenance-aware post-training method intended to replace naive redundancy-blind graph exploration, where agents treat repeated paths over the same evidence as independent corroboration. It costs a post-training stage on controlled synthetic graph data and adds benchmark instrumentation for tracking path provenance and source coverage. The central failure mode is demonstrated by the authors themselves: PAPT reduces redundant revisits on scientific claims while accuracy declines, because the agent learns to stop repeating itself but covers fewer distinct evidential sources.",
  "rationale": "The paper exposes a real failure mode in LLM graph agents, but its proposed remedy trades exploration efficiency for accuracy and source coverage on realistic tasks, so the benchmark is worth tracking while the training method is not yet safe to adopt.",
  "technique": "Provenance-aware post-training for graph agents (PAPT)",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17695",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "GraphEcho is an evaluation benchmark plus a provenance-aware post-training method intended to replace naive redundancy-blind graph exploration, where agents treat repeated paths over the same evidence as independent corroboration. It costs a post-training stage on controlled synthetic graph data and adds benchmark instrumentation for tracking path provenance and source coverage. The central failure mode is demonstrated by the authors themselves: PAPT reduces redundant revisits on scientific claims while accuracy declines, because the agent learns to stop repeating itself but covers fewer distinct evidential sources.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.17695",
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      "source_excerpt": ""
    }
  ],
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    },
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      "dimension": "security",
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      "source_page": null,
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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": [],
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  "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": ""
}