{
  "schema_version": 1,
  "updated_at": "2026-08-13",
  "publications": [
    {
      "id": "ace-272-benchmark-paper",
      "title": "ACE-272: A Retrospective Benchmark of Enterprise Trust Risks in Language-Model Endpoints",
      "type": "deep-paper",
      "date": null,
      "abstract": "A retrospective study of nine language-model endpoints evaluated across six enterprise trust domains, with explicit scope, scoring, and attribution limits.",
      "authors": [
        "Chris Ma",
        "Joanna Luo"
      ],
      "topics": [
        "enterprise AI trust",
        "benchmark methodology",
        "endpoint evaluation",
        "reliability"
      ],
      "version": "draft-1",
      "status": "in-preparation",
      "links": {
        "results": "benchmark.html",
        "methodology": "methodology.html"
      }
    },
    {
      "id": "ace-v1-1-whitepaper",
      "title": "ACE Benchmark v1.1 Technical Whitepaper",
      "type": "whitepaper",
      "date": "2026-06",
      "abstract": "The frozen technical record of the June 2026 ACE benchmark round, including methodology, scoring, results, and representative cases.",
      "authors": [
        "LogionACE"
      ],
      "topics": [
        "enterprise AI trust",
        "benchmark methodology",
        "model evaluation"
      ],
      "version": "1.1",
      "status": "archived",
      "links": {
        "page": "research.html#whitepaper",
        "pdf": "ACE_Whitepaper_v1.1.pdf",
        "results": "benchmark.html"
      }
    }
  ]
}