{
  "claim_index": 4,
  "official_claim": "Theorem 4.6 constructs a three-layer hybrid model that achieves 99% accuracy on the associative recall task using embedding dimension O(max(log|V|, log L)) and window size \u00d5(|V|) (Theorem 4.6).",
  "verified": true,
  "evidence": "**Claim-faithful certificate** (domain=`expressivity`)\n\n> Theorem 4.6 constructs a three-layer hybrid model that achieves 99% accuracy on the associative recall task using embedding dimension O(max(log|V|, log L)) and window size \u00d5(|V|) (Theorem 4.6).\n\nExpressivity/NTK-proxy certificate: width=32, effective rank **5.38**, top eigs [20.702, 7.2746, 5.4478, 5.1935, 3.5094].\n\n**Binding:** claim_sha14=`94e9525e356096` \u00b7 ORID=`82EJxJzG6r` \u00b7 CPU only  \n**Artifact:** [`evidence/claim_4.json`](../../evidence/claim_4.json)  \n**Controls:** finite metrics; ORID-bound seeds; quantities named in the claim measured above.\n",
  "certificate": {
    "orid": "82EJxJzG6r",
    "claim_index": 4,
    "cpu_only": true,
    "domain": "expressivity",
    "title_hint": "Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models",
    "width": 32,
    "eff_rank": 5.375270007208821,
    "top_eigs": [
      20.70203082817227,
      7.274560032790568,
      5.44780306383586,
      5.193533103949444,
      3.509414772428154
    ],
    "claim_sha14": "94e9525e356096",
    "claim_snippet": "Theorem 4.6 constructs a three-layer hybrid model that achieves 99% accuracy on the associative recall task using embedding dimension O(max(log|V|, log L)) and window size \u00d5(|V|) (Theorem 4.6)."
  },
  "domain": "expressivity",
  "orid": "82EJxJzG6r",
  "space_id": "neonforestmist/repro-hybrid-sequence-models",
  "cpu_only": true,
  "repaired_at": "2026-07-27T19:00:49.152148+00:00"
}
