{
  "abstract": "When a language model answers, we can usually only estimate why. We build a transformer in which every step is a Born measurement - attention $|\\langle q|k\\rangle|^4$ normalised over each row, a measured product in place of the feed-forward nonlinearity, and a Born readout. With its denominators held at their values for the input, each class probability is a polynomial in the word amplitudes, so its integrated-gradient evidence is computed by a finite quadrature with no quadrature error: the words' contributions sum to the class probability above its no-information share, to $10^{-11}$ or better. On e-SNLI natural language inference, with about 98k parameters outside the embedding, the model reaches $76.98 \\pm 0.17$% test accuracy, level with a softmax transformer of the same size ($76.62 \\pm 0.36$%). Deleting the word its evidence ranks first changes the answer in 45% of test pairs, out of the 53% any single deletion can change, against 32% for the softmax model explained the same way and 40% by its attention x gradient. Attention can look more like the words annotators highlighted, yet accounts for the answer less well: deleting the word it ranks first changes the Born Transformer's answer in 32% of pairs. The same weights compile exactly to optoelectronic optics and to qubits, and a coherent attention layer becomes one circuit equal to the model (852 CNOTs on 23 qubits). On IBM's ibm_kingston, reading only the attention overlaps by Hadamard tests, the device gave the model's answers on two short test pairs and kept or changed them under deletion as the model does; the evidence itself did not yet survive the device, and we work out the shots and calibration that measuring it on hardware would need.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.05355",
  "author_total": 3,
  "authors": [
    {
      "name": "Teerawat Tongloy"
    },
    {
      "name": "Jiraporn Pongsopa"
    },
    {
      "name": "Rangsan Jomtarak"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2ekwvr8htcrfq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.05355",
  "fulltext_url": null,
  "id": "2ekwvr8htcrfq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.05355",
  "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",
      "cs.CL"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.05355",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2ekwvr8htcrfq.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.05355",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.05355"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Reading Why: Born-Measured Attention with Exact Word Evidence on Light and Qubits",
  "title_text": "Reading Why: Born-Measured Attention with Exact Word Evidence on Light and Qubits",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:18Z",
  "url": "https://research.draconitic.com/paper/2ekwvr8htcrfq",
  "venue": "arXiv",
  "versions": []
}