{
  "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": "2610.02002",
  "title": "Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents",
  "summary": "Mem++ replaces write-time distillation of documents into facts, notes, or graph edges with whole-document storage plus read-time retrieval that filters by the question's reference date and fuses lexical and semantic rankings, requiring no generative-model calls at write time. The cost is storage of every document version in full, per-query retrieval-and-fusion latency, and shifting all reasoning about which version held at a given time onto the answering model rather than the memory layer. It can fail when the answering model cannot reconcile conflicting document versions, when lexical-semantic fusion retrieves the wrong temporal slice for ambiguously dated questions, and because the headline gains are reported on the authors' own benchmark, so transfer to a different organizational corpus is unverified.",
  "rationale": "The approach is implementable on constrained infrastructure using existing retrieval components and API answering models, but the quality gains are benchmark-specific and absolute-point rather than multiplicative, so it warrants a workload-specific pilot before adoption.",
  "technique": "Non-destructive document memory with read-time temporal retrieval",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.02002",
  "independent_implementations": 1,
  "total_implementations": 3,
  "stars": 132,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Mem++ replaces write-time distillation of documents into facts, notes, or graph edges with whole-document storage plus read-time retrieval that filters by the question's reference date and fuses lexical and semantic rankings, requiring no generative-model calls at write time. The cost is storage of every document version in full, per-query retrieval-and-fusion latency, and shifting all reasoning about which version held at a given time onto the answering model rather than the memory layer. It can fail when the answering model cannot reconcile conflicting document versions, when lexical-semantic fusion retrieves the wrong temporal slice for ambiguously dated questions, and because the headline gains are reported on the authors' own benchmark, so transfer to a different organizational corpus is unverified.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.02002",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Surpasses the strongest memory-system baseline by 8.0 to 13.1 points on OrgMemBench across two answering models; with gpt-4.1-mini it scores 2.6 points above RAG.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.02002",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
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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",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "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": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "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": ""
}