{
  "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": "2609.23892",
  "title": "Circuit-Diff: Factual Edit-based Intervention Method for Localizing Knowledge in Attribution Graphs",
  "summary": "The method replaces manual, node-by-node labeling of unlabeled CLT attribution-graph features with an automated procedure: apply a low-rank factual edit to the model and flag the graph nodes whose attribution changes under the edit. Cost is an existing Cross-Layer Transcoder plus the circuit-tracer toolchain and a factual-editing step; there is no claimed latency or memory advantage, and no deployment benefit is established. Failure modes include a frozen CLT becoming unreliable after the model weights are edited, flagged nodes capturing loose associations (history, geography of the old/new objects) rather than causally specific knowledge, and limited validation (causal patching tested on only up to 24 CounterFact edits).",
  "rationale": "This is mechanistic-interpretability research tooling for circuit discovery, not a production efficiency or capability technique, so it warrants monitoring rather than adoption in a constrained-infrastructure production setting.",
  "technique": "Factual-edit-based feature localization in CLT attribution graphs",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-20",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.23892",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces manual, node-by-node labeling of unlabeled CLT attribution-graph features with an automated procedure: apply a low-rank factual edit to the model and flag the graph nodes whose attribution changes under the edit. Cost is an existing Cross-Layer Transcoder plus the circuit-tracer toolchain and a factual-editing step; there is no claimed latency or memory advantage, and no deployment benefit is established. Failure modes include a frozen CLT becoming unreliable after the model weights are edited, flagged nodes capturing loose associations (history, geography of the old/new objects) rather than causally specific knowledge, and limited validation (causal patching tested on only up to 24 CounterFact edits).",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.23892",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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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",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "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": ""
}