{
  "abstract": "Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We show this error can be reduced by surfacing a checklist of criticisms learned from a historical review corpus. Our approach generates a tree of checklists representing coarsenings of the atomic review claim space, then selects the coarsening that maximizes a theoretically-motivated measure of correlated agreement among reviewers. We realize this approach with an LLM-assisted pipeline and test it on NeurIPS 2021 and ICLR 2022 review data. In simulated review with LLM reviewers, the learned checklist reduces the mean squared error (MSE) of a three reviewer committee by 58% on NeurIPS and 21% on ICLR, and on both venues the checklist with the highest correlated agreement has the lowest error for committees of three or more. In a human study with 20 participants, the learned NeurIPS checklist lowers the MSE of a paper ranking, measured against the NeurIPS 2021 order, from 0.48 to 0.22.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.28666",
  "author_total": 4,
  "authors": [
    {
      "name": "Xuanyou Liu"
    },
    {
      "name": "Ziyang Guo"
    },
    {
      "name": "Jason Hartline"
    },
    {
      "name": "Jessica Hullman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egvh1cvpjvatv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.28666",
  "fulltext_url": null,
  "id": "egvh1cvpjvatv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.28666",
  "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.AI"
    ],
    [
      "arxiv",
      "cs.DL"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "1207|Academic Writing and Publishing"
    ]
  ],
  "openalex": "W7223216366",
  "pdf_url": "https://arxiv.org/pdf/2610.28666",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/egvh1cvpjvatv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:29Z",
      "native_id": "2610.28666",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.28666"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Learning Checklists to Reduce Lottery Effects in Scientific Peer Review",
  "title_text": "Learning Checklists to Reduce Lottery Effects in Scientific Peer Review",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:29Z",
  "url": "https://research.draconitic.com/paper/egvh1cvpjvatv",
  "venue": "arXiv",
  "versions": []
}