{
  "abstract": "Generative models now propose millions of \"stable\" crystal structures, and their own referees have shown that stability is not discovery; no public benchmark asks whether a candidate has the physics to do a job. For superconductors the job is twofold: to pair, with a definite gap symmetry, and to become wire. We present a physics judge that answers both from a structure alone (DFT+U, a gated Wannier model, the full rank-4 RPA susceptibility on a $24^3$ mesh, the linearized gap equation, an irreducible-representation classifier, a manufacturability funnel) and measure what it says about what generators propose. With answer keys pre-registered before computing, the judge was calibrated on six known superconductors (Nb, Nb$_3$Sn, MgB$_2$, BaFe$_2$As$_2$, La$_{2-x}$Sr$_x$CuO$_4$, YBa$_2$Cu$_3$O$_7$), recovering the expected class in all six after two dated corrections. On the ruthenium analogue of the iron pnictide, with two runs and their criterion pre-registered the day before, it returns a null verdict at the same physical interaction where iron gives s$\\pm$ ($λ_1 \\le 0.0007$ versus 0.096; critical U 4.17 versus 0.97 eV); the pre-registered equal-alpha run fails the letter of the criterion, and we declare the resolution in favour of absolute U as post-hoc. The funnel reproduces the industrial map with thresholds fixed before running. Three generation audits follow: a descriptor sweep over 150 Materials Project metals re-finds the canon; 1,248 MatterGen structures yield 0 candidates that are new, stable and carry a pairing motif; mechanism filters over 47,893 compounds, validated by a blind hold-out, re-derive the community's analogies: geometry plus d-count is necessary but not sufficient. Generation re-finds what is known; judgment is the bottleneck. We propose a public benchmark over the eleven lists of AI-proposed superconductor candidates, none of which classifies gap symmetry.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.10614",
  "author_total": 1,
  "authors": [
    {
      "name": "Reinaldo Inácio"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1gf0f64jsbvmh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.10614",
  "fulltext_url": null,
  "id": "1gf0f64jsbvmh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.10614",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "cond-mat.supr-con"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7212309054",
  "pdf_url": "https://arxiv.org/pdf/2609.10614",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1gf0f64jsbvmh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2609.10614",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.10614"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Generating is not discovering: a pre-registered physics judge for AI-proposed superconductors, calibrated on six known superconductors and one negative control",
  "title_text": "Generating is not discovering: a pre-registered physics judge for AI-proposed superconductors, calibrated on six known superconductors and one negative control",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:26Z",
  "url": "https://research.draconitic.com/paper/1gf0f64jsbvmh",
  "venue": "arXiv",
  "versions": []
}