{
  "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": "2609.39765",
  "title": "MemCodex: Self-Programming Hierarchical Memory for Language Agents",
  "summary": "MemCodex replaces fixed, predefined memory workflows (and adaptive methods limited to a predefined design space) with a hierarchy of executable memory programs whose construction, indexing, retrieval, and routing are evolved automatically, traversed coarse-to-fine at query time. The cost is substantial system complexity: a program-evolution search loop, a unified runtime (MemArena) to host heterogeneous memory backends, and added infrastructure for maintaining and rewriting memory layers, none of which runs without ongoing LLM calls for evolution and routing. Failure modes include evolved retrieval programs degrading silently as workloads drift, early stopping at coarse layers missing fine-grained evidence, and the reported gains not transferring if the reader's tasks differ from the evaluation benchmark.",
  "rationale": "The claimed quality and token-efficiency gains are relevant to API-based agent deployments, but the evolutionary search machinery and unified runtime require workload-specific validation on the reader's own tasks before production adoption.",
  "technique": "Self-evolving hierarchical executable agent memory",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39765",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "MemCodex replaces fixed, predefined memory workflows (and adaptive methods limited to a predefined design space) with a hierarchy of executable memory programs whose construction, indexing, retrieval, and routing are evolved automatically, traversed coarse-to-fine at query time. The cost is substantial system complexity: a program-evolution search loop, a unified runtime (MemArena) to host heterogeneous memory backends, and added infrastructure for maintaining and rewriting memory layers, none of which runs without ongoing LLM calls for evolution and routing. Failure modes include evolved retrieval programs degrading silently as workloads drift, early stopping at coarse layers missing fine-grained evidence, and the reported gains not transferring if the reader's tasks differ from the evaluation benchmark.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39765",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "The paper reports a 10.1% relative improvement in average task success over the strongest adaptive-memory baseline, with 3.4x fewer context tokens and 2.1x faster inference.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39765",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "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",
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      "finding": "",
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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,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "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": ""
}