{
  "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": "2609.19502",
  "title": "Reputation as Community Memory for the Agentic Web",
  "summary": "The method replaces private per-agent memory of tool, service, and data-source reliability with a shared reputation platform (Cairn) that aggregates evidence-backed ratings via a time-decayed Beta model with confidence shrinkage and semantic retrieval over reviewer rationales. Its cost is the infrastructure and governance of a community platform—submission of evidence-backed ratings, aggregation maintenance, and retrieval overhead per resource query—plus dependence on participation from many independent agents. It can fail under adversarial behavior the simulation only partially covers: coordinated lying, collusion, and camouflage can still bias aggregate scores, and with low participation the shrinkage mechanism yields low-confidence, uninformative ratings.",
  "rationale": "The approach requires a multi-agent community and platform infrastructure that a small team with constrained infrastructure cannot realistically bootstrap or benefit from in production today.",
  "technique": "Collective reputation memory for agent resource selection",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19502",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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    {
      "claim_id": "claim-01",
      "statement": "The method replaces private per-agent memory of tool, service, and data-source reliability with a shared reputation platform (Cairn) that aggregates evidence-backed ratings via a time-decayed Beta model with confidence shrinkage and semantic retrieval over reviewer rationales. Its cost is the infrastructure and governance of a community platform—submission of evidence-backed ratings, aggregation maintenance, and retrieval overhead per resource query—plus dependence on participation from many independent agents. It can fail under adversarial behavior the simulation only partially covers: coordinated lying, collusion, and camouflage can still bias aggregate scores, and with low participation the shrinkage mechanism yields low-confidence, uninformative ratings.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19502",
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      "source_excerpt": ""
    }
  ],
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      "next_check": "",
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      "dimension": "operations",
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "",
  "exposures_reviewed_at": "",
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}