{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2609.24635",
  "title": "Written as a Record, Read as an Address: What a Forward Pass Leaves in an Operation's KV Cache",
  "summary": "This is a mechanistic interpretability study, not a deployable technique; it replaces nothing in a production stack, instead analyzing what a forward pass writes into operation-span KV entries (a routing record plus a narrowly localized payload readable at layers 12-17). Its cost is an extra trained reader model, cache recomputation, and a reported drop in open-book accuracy, and it is bounded by training coverage of the operations tested. For adoption the failure mode is categorical: the findings concern how models internally record state in synthetic tasks and do not yield a validated inference-time mechanism, so treating them as a production feature risks misapplication.",
  "rationale": "The paper characterizes internal KV-cache representations for interpretability purposes and offers no production-ready method with a quantified efficiency or quality gain for a constrained-infrastructure team.",
  "technique": "KV-cache operation-span probing and transplant analysis",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24635",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This is a mechanistic interpretability study, not a deployable technique; it replaces nothing in a production stack, instead analyzing what a forward pass writes into operation-span KV entries (a routing record plus a narrowly localized payload readable at layers 12-17). Its cost is an extra trained reader model, cache recomputation, and a reported drop in open-book accuracy, and it is bounded by training coverage of the operations tested. For adoption the failure mode is categorical: the findings concern how models internally record state in synthetic tasks and do not yield a validated inference-time mechanism, so treating them as a production feature risks misapplication.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.24635",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "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",
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      "finding": "",
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      "next_check": "",
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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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}