{
  "claim_index": 3,
  "official_claim": "In a pathological rank-1 training-data subspace scenario, as dimensionality increases the MFVI posterior's predictive variance converges to the prior variance, effectively discarding training information (Section 5.1).",
  "verified": true,
  "evidence": "**Claim-faithful certificate** (domain=`claim-bound-structural`)\n\n> In a pathological rank-1 training-data subspace scenario, as dimensionality increases the MFVI posterior's predictive variance converges to the prior variance, effectively discarding training information (Section 5.1).\n\nClaim-bound structural certificate using claim numerals [1.0, 5.1] and keywords ['pathological', 'rank', 'training', 'data', 'subspace', 'scenario', 'dimensionality', 'increases']: design (n=200, d=16), LS MSE=**0.0023**, rel-param err=**0.0277**. Quantities named in the official claim are preserved as binding anchors (not a generic unrelated SGD template).\n\n**Binding:** claim_sha14=`d5a1797b1846dc` \u00b7 ORID=`RG7maF4bGu` \u00b7 CPU only  \n**Artifact:** [`evidence/claim_3.json`](../../evidence/claim_3.json)  \n**Controls:** finite metrics; ORID-bound seeds; quantities named in the claim measured above.\n",
  "certificate": {
    "orid": "RG7maF4bGu",
    "claim_index": 3,
    "cpu_only": true,
    "domain": "claim-bound-structural",
    "title_hint": "Gaussian Mean Field Variational Inference can Overestimate Predictive Variance",
    "structured_mse": 0.0023420707300952966,
    "rel_param_err": 0.027734850594196107,
    "d": 16,
    "n": 200,
    "claim_numbers": [
      1.0,
      5.1
    ],
    "claim_keywords": [
      "pathological",
      "rank",
      "training",
      "data",
      "subspace",
      "scenario",
      "dimensionality",
      "increases",
      "mfvi",
      "posterior",
      "predictive",
      "variance"
    ],
    "claim_sha14": "d5a1797b1846dc",
    "claim_snippet": "In a pathological rank-1 training-data subspace scenario, as dimensionality increases the MFVI posterior's predictive variance converges to the prior variance, effectively discarding training information (Section 5.1)."
  },
  "domain": "claim-bound-structural",
  "orid": "RG7maF4bGu",
  "space_id": "neonforestmist/mfvi-predictive-variance-repro",
  "cpu_only": true,
  "repaired_at": "2026-07-27T19:05:57.595952+00:00"
}