{
  "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": "2610.20191",
  "title": "HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation",
  "summary": "Replaces single-center medication-recommendation models and naive multi-center transfer learning with a hierarchical memory that separates cross-hospital clinical experience from hospital-specific prescribing residuals, plus a safety-aware set decoder handling drug-drug interactions and formulary constraints. Costs include maintaining per-patient attribution records and dual memory stores, added retrieval and decoding complexity, and dependence on multi-center EHR data that is difficult to obtain. Can fail through privacy and regulatory constraints on cross-institution clinical data, poor transfer when a hospital's residual patterns dominate, and safety decoding that cannot guarantee absence of contraindicated combinations in deployment.",
  "rationale": "The method targets a regulated clinical domain requiring multi-center EHR access and validation that a small team with one GPU and limited budget cannot realistically deploy, so it merits monitoring rather than adoption.",
  "technique": "Hierarchical disentangled agentic memory for multi-center medication recommendation",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20191",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces single-center medication-recommendation models and naive multi-center transfer learning with a hierarchical memory that separates cross-hospital clinical experience from hospital-specific prescribing residuals, plus a safety-aware set decoder handling drug-drug interactions and formulary constraints. Costs include maintaining per-patient attribution records and dual memory stores, added retrieval and decoding complexity, and dependence on multi-center EHR data that is difficult to obtain. Can fail through privacy and regulatory constraints on cross-institution clinical data, poor transfer when a hospital's residual patterns dominate, and safety decoding that cannot guarantee absence of contraindicated combinations in deployment.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20191",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Abstract states HEM-Med outperforms representative transfer learning, LLM-based, and medication-recommendation baselines on recommendation accuracy and safety on a multi-center eICU dataset, but reports no quantified margin.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20191",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "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": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "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": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}