{
  "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": "2610.21405",
  "title": "MemCut: Every Secret Leaves a Trail in the Long-Term Memory of LLM Agents",
  "summary": "MemCut replaces ad hoc privacy testing of agent long-term memory with a 456-secret benchmark with annotated evidence trails, plus native and residual recovery metrics and three reference deletion baselines across five memory designs and four LLMs. Adoption costs are evaluation compute, storage of annotated evidence traces, and engineering to widen deletion candidate pools; findings indicate that LLM-guided deletion of ten extra turns from twenty retrieval candidates still leaves 39-76% residual recovery. It can fail if deletion only targets directly retrieved objects, since secrets survive in paraphrased or never-directly-stated evidence, and candidate coverage is the identified bottleneck rather than deletion quality.",
  "rationale": "Engineers deploying memory-enabled agents subject to deletion requests need this evaluation and its widened-candidate-pool deletion practice, but the residual recovery results must be validated on their own memory architecture and data before relying on them for compliance.",
  "technique": "Benchmarking and deletion baselines for user-requested memory forgetting",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21405",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "MemCut replaces ad hoc privacy testing of agent long-term memory with a 456-secret benchmark with annotated evidence trails, plus native and residual recovery metrics and three reference deletion baselines across five memory designs and four LLMs. Adoption costs are evaluation compute, storage of annotated evidence traces, and engineering to widen deletion candidate pools; findings indicate that LLM-guided deletion of ten extra turns from twenty retrieval candidates still leaves 39-76% residual recovery. It can fail if deletion only targets directly retrieved objects, since secrets survive in paraphrased or never-directly-stated evidence, and candidate coverage is the identified bottleneck rather than deletion quality.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21405",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "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": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}