{
  "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": "2610.30474",
  "title": "Rethinking SAE Evaluation: An Atomic Interpretable Unit Framework Reveals Hidden Polysemanticity and Redundancy",
  "summary": "The framework replaces ad hoc SAE evaluation metrics (sparsity, dead-feature rate, reconstruction fidelity, monosemanticity checks) with a formalized objective—features should form an atomic interpretable unit basis—operationalized through dominance-aware proxy metrics such as DAME. It costs only additional evaluation computation on existing SAEs and models, requiring no retraining, but adopting the benchmarking suite adds integration and interpretation overhead for a small team. It can fail if proxy metrics do not correspond to downstream attribution or intervention usefulness, and the finding that dominant features are less interpretable and more redundant means SAEs passing conventional checks may still mislead circuit tracing or steering interventions.",
  "rationale": "For a practitioner using SAEs for attribution or intervention, running dominance-aware evaluation before deployment is a low-cost validation step whose workload-specific value should be confirmed before becoming standard practice.",
  "technique": "Atomic interpretable unit evaluation for sparse autoencoders",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-10-21",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30474",
  "independent_implementations": 2,
  "total_implementations": 4,
  "stars": 4,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The framework replaces ad hoc SAE evaluation metrics (sparsity, dead-feature rate, reconstruction fidelity, monosemanticity checks) with a formalized objective—features should form an atomic interpretable unit basis—operationalized through dominance-aware proxy metrics such as DAME. It costs only additional evaluation computation on existing SAEs and models, requiring no retraining, but adopting the benchmarking suite adds integration and interpretation overhead for a small team. It can fail if proxy metrics do not correspond to downstream attribution or intervention usefulness, and the finding that dominant features are less interpretable and more redundant means SAEs passing conventional checks may still mislead circuit tracing or steering interventions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30474",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "dimension": "latency",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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      "finding": "",
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      "next_check": "",
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      "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": ""
}