{
  "schema": "certiv_public_ai_discovery_dossier_v1",
  "corpus_id": "certiv-ai-discovery-2026-07-16",
  "reviewed_at": "2026-07-16",
  "evidence_boundary": "These are Certiv-authored test dossiers, not author-issued Certiv receipts. A pinned PDF proves file identity only. A passing selected replay records one bounded observation; it does not independently reproduce a paper's central claim or establish truth, novelty, priority, safety, or correct interpretation.",
  "case": {
    "slug": "ai-newton",
    "case_type": "discovery-system",
    "domain": "Physics",
    "title": "AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge",
    "authors": {
      "display": "You-Le Fang, Dong-Shan Jian, Xiang Li, and Yan-Qing Ma",
      "count": 4
    },
    "paper": {
      "arxiv_id": "2504.01538",
      "version": "v2",
      "submitted": "2025-04-02",
      "version_date": "2025-12-11",
      "abs_url": "https://arxiv.org/abs/2504.01538v2",
      "pdf_url": "https://arxiv.org/pdf/2504.01538v2",
      "pdf_sha256": "bce6758e3ec33fe715b2df7132733107f98f1e928abb9ead89b9b7e2b3a8cdc1",
      "pdf_bytes": 5198153,
      "pages": 6,
      "extracted_text_bytes": 28334,
      "parser_observations": [
        "Recognized as a PDF and not encrypted.",
        "pdfinfo reported JavaScript: no.",
        "pdfdetach reported no embedded files.",
        "pdftotext completed successfully."
      ]
    },
    "selection_rationale": "A public concept-driven physics system whose proprietary symbolic dependency and multi-day benchmark make resource and licensing boundaries concrete.",
    "claim": {
      "reported": "The authors report that AI-Newton derived general physical laws from raw multi-experiment data without prior physical knowledge, rediscovering several established laws.",
      "certiv_observed": "The exact v2 PDF and repository revision were pinned. The documented stack requires Python, Rust, CUDA, and Maple 2024; Maple was not present in the review environment.",
      "not_established": "The benchmark, days-long full run, absence of prior knowledge, robustness to noise, and general discovery capability were not independently reproduced. The reported outputs are rediscoveries of known laws, not newly established laws of nature."
    },
    "verification_level": "artifact-inventoried",
    "central_claim_independently_reproduced": false,
    "checks": [
      {
        "id": "pdf-identity",
        "status": "observed",
        "label": "Exact PDF identity",
        "observation": "The downloaded v2 PDF matched the recorded SHA-256, byte count, and page count.",
        "boundary": "This establishes file identity only."
      },
      {
        "id": "repository-inventory",
        "status": "observed",
        "label": "Repository and dependency inventory",
        "observation": "The repository was pinned at c143e865be42e067faf64cd8117cbeffd0fccfb6; its README, Cargo.lock, and sample knowledge file were separately hashed.",
        "boundary": "Inventory records what was reviewed, not whether the system works as claimed."
      },
      {
        "id": "full-benchmark",
        "status": "blocked",
        "label": "Full benchmark replay",
        "observation": "Maple 2024 was unavailable, and the documented full test requires several days on high-end servers.",
        "boundary": "The missing proprietary dependency and compute budget prevented a faithful run."
      }
    ],
    "artifacts": [
      {
        "label": "AI-Newton repository",
        "url": "https://github.com/Science-Discovery/AI-Newton",
        "observed_revision": "c143e865be42e067faf64cd8117cbeffd0fccfb6",
        "note": "README SHA-256 e35a6fd49f3dfa9600b400d906ef46146e3ec9d0c179c985a313b89fbfe2a4ab; Cargo.lock SHA-256 1e75c6930405e1ec20be9936c6ca9d75e8b42f3d8a61462d33a18d244cf8920c; sample knowledge SHA-256 a7524f42a48dc6165eabd65c9fc3696fdd652df8dc1e4d251bdc93ebdb956df9."
      }
    ],
    "red_team_findings": [
      "A proprietary Maple dependency prevents a fully open replay even though the repository is public.",
      "The short benchmark and full multi-day benchmark have materially different resource requirements.",
      "Rediscovering known laws is evidence about a system's behavior, not a new physical discovery."
    ],
    "certiv_mapping": {
      "inputs": "Exact PDF, commit-pinned code, and hashes for key dependency and knowledge files.",
      "checks": "Artifact and prerequisite inventory; no benchmark execution.",
      "receipt_boundary": "artifact-inventoried; replay blocked by Maple and compute requirements",
      "missing_for_deeper_verification": "Licensed Maple environment, exact CUDA stack, experiment data, complete locked environment, multi-day compute, and independent evaluation of recovered laws."
    },
    "sources": [
      {
        "label": "Exact arXiv record",
        "url": "https://arxiv.org/abs/2504.01538v2"
      },
      {
        "label": "Public repository",
        "url": "https://github.com/Science-Discovery/AI-Newton"
      }
    ]
  }
}