{
  "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": "2609.27588",
  "title": "The Capability Manifold and ML Scaling Laws",
  "summary": "The paper proposes an analytical framework, the capability manifold, that replaces ad hoc interpretation of separate loss-based scaling laws with a multidimensional mapping from pre-training, post-training, and test-time resources to downstream capabilities, with Jacobians quantifying sensitivity and interactions. It costs nothing to deploy but offers no new model, method, or empirical efficiency gain; it is a mathematical formalization that re-embeds known Kaplan-, Chinchilla-, and test-time-compute laws. It can fail as a decision tool if its bounded scaling functions are miscalibrated to a specific workload, since the abstract reports no empirical validation against measured capability data.",
  "rationale": "This is a theoretical unification with no quantified performance claim and no deployable artifact, so it is relevant context for capacity planning but not something to adopt or test on constrained infrastructure.",
  "technique": "Capability manifold framework unifying scaling laws",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27588",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper proposes an analytical framework, the capability manifold, that replaces ad hoc interpretation of separate loss-based scaling laws with a multidimensional mapping from pre-training, post-training, and test-time resources to downstream capabilities, with Jacobians quantifying sensitivity and interactions. It costs nothing to deploy but offers no new model, method, or empirical efficiency gain; it is a mathematical formalization that re-embeds known Kaplan-, Chinchilla-, and test-time-compute laws. It can fail as a decision tool if its bounded scaling functions are miscalibrated to a specific workload, since the abstract reports no empirical validation against measured capability data.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.27588",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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      "dimension": "operations",
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    },
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      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "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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      "limitation": "",
      "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": ""
}