{
  "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": "2610.30698",
  "title": "Governance-State Corruption in Long-Term Memory for LLM Agents: A Cross-Model Stress Test",
  "summary": "This replaces evaluation of long-term memory solely by retrieval accuracy with an additional control-plane check that verifies validity-state enforcement over which records reach model context. It costs an offline auditing harness and matched fault-pair construction on existing benchmarks, with no added inference latency or model changes. It can fail silently in production if the memory layer lacks deterministic state enforcement, since retrieval can be correct while the operative context is wrong, and the frozen-scorer effects remain descriptive rather than validated semantic measures.",
  "rationale": "The reader runs LLM agents in production and can add governance-state checks to their memory layer cheaply, but the measured impact must be validated on their own workload before trusting the reported effect sizes.",
  "technique": "Governance-state integrity testing for agent memory",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-22",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30698",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 14,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This replaces evaluation of long-term memory solely by retrieval accuracy with an additional control-plane check that verifies validity-state enforcement over which records reach model context. It costs an offline auditing harness and matched fault-pair construction on existing benchmarks, with no added inference latency or model changes. It can fail silently in production if the memory layer lacks deterministic state enforcement, since retrieval can be correct while the operative context is wrong, and the frozen-scorer effects remain descriptive rather than validated semantic measures.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30698",
      "source_page": null,
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    },
    {
      "claim_id": "claim-02",
      "statement": "Corrupting memory governance state drops LongMemEval binary correctness by 40.54 percentage points (95% CI [30.32, 50.71]) and Memora FAMA by 30.60 points; oracle-backed enforcement restores the correct projection in 100% of 2,080 fault pairs.",
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      "source_url": "https://arxiv.org/abs/2610.30698",
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    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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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_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}