{
  "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": "2609.31098",
  "title": "The Residual Stream's Effective Depth",
  "summary": "The method does not replace any production component; it is an interpretability diagnostic that measures representation-similarity decay across layers and aggregates it into a single scalar, replacing ad hoc layer-similarity inspection with a calibrated reference bound. Its cost is negligible compute (cosine similarities of hidden states, feasible on one 24 GB GPU) but it delivers no latency, memory, cost, or quality improvement. It can fail if misread: the authors state it is a global accumulated-state diagnostic, not a capability score, pruning criterion, or signal that depth is unused, so acting on it for compression or model selection would be unsupported.",
  "rationale": "This is a diagnostic finding about transformer residual dynamics with no quantified production gain and explicit author caution against using it as a pruning or capability metric, so it is relevant background but not an adoptable production technique.",
  "technique": "Effective depth diagnostic for residual streams",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31098",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method does not replace any production component; it is an interpretability diagnostic that measures representation-similarity decay across layers and aggregates it into a single scalar, replacing ad hoc layer-similarity inspection with a calibrated reference bound. Its cost is negligible compute (cosine similarities of hidden states, feasible on one 24 GB GPU) but it delivers no latency, memory, cost, or quality improvement. It can fail if misread: the authors state it is a global accumulated-state diagnostic, not a capability score, pruning criterion, or signal that depth is unused, so acting on it for compression or model selection would be unsupported.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.31098",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "next_check": "",
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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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": ""
}