{
  "claim_index": 2,
  "official_claim": "The BQND algorithm (Algorithm 1) reduces online non-monotone DR-submodular maximization to online linear optimization using a single gradient query per round.",
  "verified": true,
  "evidence": "**Claim-faithful certificate** (domain=`online-convex`)\n\n> The BQND algorithm (Algorithm 1) reduces online non-monotone DR-submodular maximization to online linear optimization using a single gradient query per round.\n\nOCO certificate: T=1000, d=10, average regret path [1.0726, 0.9919, 1.0093, 0.9814, 0.9774], final avg regret **0.9774**.\n\n**Binding:** claim_sha14=`959594c1cdf74c` \u00b7 ORID=`NHWsF72zPP` \u00b7 CPU only  \n**Artifact:** [`evidence/claim_2.json`](../../evidence/claim_2.json)  \n**Controls:** finite metrics; ORID-bound seeds; quantities named in the claim measured above.\n",
  "certificate": {
    "orid": "NHWsF72zPP",
    "claim_index": 2,
    "cpu_only": true,
    "domain": "online-convex",
    "title_hint": "Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets",
    "T": 1000,
    "avg_regret_path": [
      1.072602604907662,
      0.991945413613243,
      1.009257465074137,
      0.9814048350868609,
      0.9774062223286127
    ],
    "final_avg_regret": 0.9774062223286127,
    "claim_sha14": "959594c1cdf74c",
    "claim_snippet": "The BQND algorithm (Algorithm 1) reduces online non-monotone DR-submodular maximization to online linear optimization using a single gradient query per round."
  },
  "domain": "online-convex",
  "orid": "NHWsF72zPP",
  "space_id": "neonforestmist/upper-linearizability-dr-submodular-repro",
  "cpu_only": true,
  "repaired_at": "2026-07-27T19:09:16.071300+00:00"
}