{
  "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": "2609.37725",
  "title": "Context Language Models",
  "summary": "Replaces externally engineered context-management harnesses (truncation, summarization, retrieval heuristics) with the model itself editing its context as a file, using only prompting of existing models at the zero-shot level. The zero-shot variant costs prompt overhead for file-update operations and adds harness complexity for parsing and applying edits, while the reported RL training and Suffix Cache Reuse serving gains require training infrastructure and custom serving beyond the reader's budget. The model can delete or corrupt information it later needs, behavior depends heavily on base-model capability, and benchmark gains may not transfer to the reader's specific workloads without validation.",
  "rationale": "The zero-shot variant is implementable with API models and modest harness work, but the reported gains are benchmark-specific and the failure mode of unrecoverable context loss requires workload-specific validation before production reliance.",
  "technique": "Model-managed context as editable file",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37725",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 156,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces externally engineered context-management harnesses (truncation, summarization, retrieval heuristics) with the model itself editing its context as a file, using only prompting of existing models at the zero-shot level. The zero-shot variant costs prompt overhead for file-update operations and adds harness complexity for parsing and applying edits, while the reported RL training and Suffix Cache Reuse serving gains require training infrastructure and custom serving beyond the reader's budget. The model can delete or corrupt information it later needs, behavior depends heavily on base-model capability, and benchmark gains may not transfer to the reader's specific workloads without validation.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.37725",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Zero-shot CLMs outperform SOTA context management: +11.4% accuracy with 21.5% fewer FLOPs on BrowseComp-Plus; +5% score with 59% fewer FLOPs on 12-hour EdgeBench; RL-trained Qwen3.5-9B gains 47.6% on BrowseComp-Plus with 12% fewer FLOPs.",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.37725",
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    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
      "source_url": "",
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    },
    {
      "dimension": "compute",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}