{
  "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": "2608.15008",
  "title": "Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents",
  "summary": "This is a benchmark study, not a method: it replaces the implicit practice of committing to a single memory substrate (dense retrieval, sparse index, text record, structural or hierarchical store) with the finding that no substrate dominates and that retrieval breadth should be routed by task regime and history length. Adopting its guidance costs engineering effort to instrument memory metrics and to build routing logic over existing memory stores, with no additional model or training cost. It can fail because the substrate rankings may not transfer to a different backbone model, benchmark, or production workload, and code was not yet released, so reproducing the harness requires independent implementation.",
  "rationale": "The paper offers directly applicable design guidance for memory-augmented agents on constrained infrastructure, but its conclusions are workload-dependent and unreleased code means the reader must validate routing decisions on their own data before production adoption.",
  "technique": "Evaluation-driven routing of agent memory substrates",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-08-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2608.15008",
  "independent_implementations": 2,
  "total_implementations": 2,
  "stars": 2,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This is a benchmark study, not a method: it replaces the implicit practice of committing to a single memory substrate (dense retrieval, sparse index, text record, structural or hierarchical store) with the finding that no substrate dominates and that retrieval breadth should be routed by task regime and history length. Adopting its guidance costs engineering effort to instrument memory metrics and to build routing logic over existing memory stores, with no additional model or training cost. It can fail because the substrate rankings may not transfer to a different backbone model, benchmark, or production workload, and code was not yet released, so reproducing the harness requires independent implementation.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2608.15008",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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    },
    {
      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "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": "no_equations",
  "core_kind": "",
  "equations_fetched_at": "2026-09-17",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}