{
  "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": "2610.21170",
  "title": "MemScope: Evolving Agent Memory Architectures through Localized Code Repair",
  "summary": "MemScope replaces coarse feedback-driven (score- or summary-based) self-evolution of memory architectures with a Patch Agent that inspects raw execution traces, runtime memory state, and architecture code to apply bounded, in-place edits. It costs an additional tool-using agent loop over traces and code during the offline evolution phase, plus trace/memory-state logging infrastructure, rather than inference-time overhead. It can fail when failure localization from traces is incorrect, when bounded edits overfit the evaluation tasks (gains on ALFWorld are larger than on xBench), or when provider-identity and memory-compatibility constraints block needed changes, and results are reported on specific benchmarks that may not transfer to the reader's workload.",
  "rationale": "The approach is implementable with third-party APIs and no training, but its quality gains vary substantially by benchmark and model, so the reader should validate it on their own task distribution before production adoption.",
  "technique": "Localized code repair for agent memory architectures",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21170",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2610.21170",
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    {
      "claim_id": "claim-02",
      "statement": "ALFWorld unseen-task success rises from 50.75% to 71.64%; xBench improves 49 to 55 (DeepSeek V3.2) and 50 to 53 (Kimi K2.7); GAIA variant reaches 86.67% vs 63.33% for summary-only feedback, with a median 4.4% code-change ratio.",
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      "source_url": "https://arxiv.org/abs/2610.21170",
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  "exposure_map": [
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      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "security",
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      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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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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}