{
  "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": "2608.21867",
  "title": "MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance",
  "summary": "MemGuard replaces one-shot verifier filtering of agent memory with persistent lifecycle metadata (reward, confidence, label, uncertainty) attached to each memory candidate and reused for retrieval, conflict resolution, summarization, and archival. The cost is a score-token verification pass on every candidate memory plus storing and maintaining the metadata through the memory lifecycle, adding inference and engineering overhead to each memory write and retrieval. It can fail if the verifier's scores are miscalibrated for the target domain, if metadata-driven conflict resolution discards useful experience, and its reported gains may not transfer to workloads unlike the evaluated terminal, coding, and web benchmarks.",
  "rationale": "The method is implementable on constrained infrastructure via API calls for verification, but its success-rate gains are measured on specific agent benchmarks and require validation that verifier quality and metadata overhead pay off on the reader's actual workload.",
  "technique": "Persistent verifier-signal metadata for agent memory",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-08-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2608.21867",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 4,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "MemGuard replaces one-shot verifier filtering of agent memory with persistent lifecycle metadata (reward, confidence, label, uncertainty) attached to each memory candidate and reused for retrieval, conflict resolution, summarization, and archival. The cost is a score-token verification pass on every candidate memory plus storing and maintaining the metadata through the memory lifecycle, adding inference and engineering overhead to each memory write and retrieval. It can fail if the verifier's scores are miscalibrated for the target domain, if metadata-driven conflict resolution discards useful experience, and its reported gains may not transfer to workloads unlike the evaluated terminal, coding, and web benchmarks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2608.21867",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Best success metric and lowest average steps in all 16 backbone-benchmark settings; up to +7.9 success-rate points on WebArena, +5.6 step-success-rate points on Mind2Web, and +2.4-3.5 points on terminal and software-engineering benchmarks over ReasoningBank, averaged over five seeds.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2608.21867",
      "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,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "selector_failed",
  "core_kind": "",
  "equations_fetched_at": "2026-09-19",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}