{
  "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": "2609.18182",
  "title": "WFM: Wiki Foundation Model for Complex Agentic Reasoning",
  "summary": "Replaces sparse knowledge-graph embeddings for LLM-Wiki agent memory with a learned query-conditioned attentive encoder over a Wiki Graph schema. Costs include training a foundation model that requires distributed multi-GPU clusters with NCCL collectives, which exceeds a single 24 GB GPU and a limited cloud budget. Can fail if benchmark gains do not transfer to the reader's corpus, and no released inference-cost or serving data confirms deployability at small scale.",
  "rationale": "The method's core contribution is distributed-training infrastructure and foundation-model training, both unavailable under the reader's constraints, so it is worth tracking rather than adopting.",
  "technique": "Wiki foundation model for agent memory retrieval",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18182",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces sparse knowledge-graph embeddings for LLM-Wiki agent memory with a learned query-conditioned attentive encoder over a Wiki Graph schema. Costs include training a foundation model that requires distributed multi-GPU clusters with NCCL collectives, which exceeds a single 24 GB GPU and a limited cloud budget. Can fail if benchmark gains do not transfer to the reader's corpus, and no released inference-cost or serving data confirms deployability at small scale.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18182",
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      "source_excerpt": ""
    },
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      "source_url": "https://arxiv.org/abs/2609.18182",
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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_url": "",
      "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",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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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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  "exposures_source_sha256": ""
}