{
  "abstract": "Dark matter constraint repositories are critical community infrastructure, yet the most widely-used are volunteer-maintained, a sustainability risk as results accelerate. Limits are typically published as figures rather than machine-readable records, compounding the burden. We present a large language model (LLM) pipeline that monitors arXiv, extracts limit curves, integrates them as code, and opens pull requests for human review. On a 329-paper benchmark of measured limits graded against the upstream-curated repository, it identifies the coupling type for 98.1% of gradable papers. The 81% yielding a comparable extraction reach a median residual of 0.17 dex from a single model read, and 51% of all papers land within a factor of two of the curator's curve. The LLM is confined to perception; arithmetic, unit conversion, and the guards that reject mis-extractions are deterministic, testable code. Deployed, the pipeline has generated limit proposals, but none have merged: governance of AI-generated scientific data is itself unsolved.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.21658",
  "author_total": 2,
  "authors": [
    {
      "name": "Lanqing Yuan"
    },
    {
      "name": "Karthik Ramanathan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ap3qpb96wr8zn.bib",
  "cited_by": 1,
  "doi": "10.48550/arxiv.2606.21658",
  "fulltext_url": null,
  "id": "ap3qpb96wr8zn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.21658",
  "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",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "openalex",
      "3106|Dark Matter and Cosmic Phenomena"
    ]
  ],
  "openalex": "W7165678991",
  "pdf_url": "https://arxiv.org/pdf/2606.21658",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ap3qpb96wr8zn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2606.21658",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.21658"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Towards LLM-Powered Automation of a Dark Matter Constraint Repository",
  "title_text": "Towards LLM-Powered Automation of a Dark Matter Constraint Repository",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:33Z",
  "url": "https://research.draconitic.com/paper/ap3qpb96wr8zn",
  "venue": "arXiv",
  "versions": []
}