{
  "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": "2609.30813",
  "title": "A Benchmark and Diagnostic Study of Epistemic Admission in Shared Agent Memory",
  "summary": "This replaces unrestricted or deduplication-based admission of claims into a shared multi-agent memory store with admission policies that gate on declared source type and track source lineage, evaluated via the CPB benchmark. The cost is maintaining lineage metadata for every write and retrieval, plus the coverage loss observed with stricter policies, which reject many true claims alongside false ones. It can fail because no non-oracle policy reliably rejects false claims across verbatim copies, paraphrases, and paraphrases declared authoritative, and any false belief that does enter memory is asserted downstream with near-certainty.",
  "rationale": "The reader can implement source-type gating and lineage logging in an existing RAG or agent memory stack at negligible infrastructure cost, but the paper shows the residual failure modes are severe, so adoption should be validated on the reader's own workload before production reliance.",
  "technique": "Source-lineage gating for shared agent memory admission",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.30813",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This replaces unrestricted or deduplication-based admission of claims into a shared multi-agent memory store with admission policies that gate on declared source type and track source lineage, evaluated via the CPB benchmark. The cost is maintaining lineage metadata for every write and retrieval, plus the coverage loss observed with stricter policies, which reject many true claims alongside false ones. It can fail because no non-oracle policy reliably rejects false claims across verbatim copies, paraphrases, and paraphrases declared authoritative, and any false belief that does enter memory is asserted downstream with near-certainty.",
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      "source_url": "https://arxiv.org/abs/2609.30813",
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      "statement": "Gating admission on declared source type reduces false adoption to 0.06-0.09, versus 0.22-0.47 for other answering policies; however, once an uncontested false belief enters memory, a consumer asserts it in 0.97-0.99 of probes.",
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      "source_url": "https://arxiv.org/abs/2609.30813",
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      "dimension": "compatibility",
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      "dimension": "security",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}