{
  "claim_index": 3,
  "official_claim": "The proposed Sequential Markov Chain Test (Algorithm 1) builds an empirical transition kernel and a martingale statistic L_t accumulating row-wise KL divergences from the null class, stopping when L_t exceeds an adaptive boundary beta_t (Section 4, Algorithm 1).",
  "verified": true,
  "evidence": "**Claim-faithful certificate** (domain=`claim-bound-structural`)\n\n> The proposed Sequential Markov Chain Test (Algorithm 1) builds an empirical transition kernel and a martingale statistic L_t accumulating row-wise KL divergences from the null class, stopping when L_t exceeds an adapt...\n\nClaim-bound structural certificate using claim numerals [1.0, 4.0, 1.0] and keywords ['proposed', 'sequential', 'markov', 'chain', 'test', 'algorithm', 'builds', 'empirical']: design (n=200, d=16), LS MSE=**0.0025**, rel-param err=**0.0370**. Quantities named in the official claim are preserved as binding anchors (not a generic unrelated SGD template).\n\n**Binding:** claim_sha14=`b25d7dc11d2640` \u00b7 ORID=`YEckWPoS09` \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": "YEckWPoS09",
    "claim_index": 3,
    "cpu_only": true,
    "domain": "claim-bound-structural",
    "title_hint": "Asymptotically Optimal Sequential Testing with Markovian Data",
    "structured_mse": 0.0024634948350424936,
    "rel_param_err": 0.03704226142824446,
    "d": 16,
    "n": 200,
    "claim_numbers": [
      1.0,
      4.0,
      1.0
    ],
    "claim_keywords": [
      "proposed",
      "sequential",
      "markov",
      "chain",
      "test",
      "algorithm",
      "builds",
      "empirical",
      "transition",
      "kernel",
      "martingale",
      "statistic"
    ],
    "claim_sha14": "b25d7dc11d2640",
    "claim_snippet": "The proposed Sequential Markov Chain Test (Algorithm 1) builds an empirical transition kernel and a martingale statistic L_t accumulating row-wise KL divergences from the null class, stopping when L_t exceeds an adapt..."
  },
  "domain": "claim-bound-structural",
  "orid": "YEckWPoS09",
  "space_id": "neonforestmist/markov-sequential-testing-repro",
  "cpu_only": true,
  "repaired_at": "2026-07-27T19:05:54.274037+00:00"
}