{
  "abstract": "Large language models (LLMs) are increasingly used to formulate optimization models from natural-language problem descriptions, yet realistic operations research (OR) requests are often incomplete: missing objectives, constraints, or business rules can change the resulting mathematical program. Existing evaluations largely assume a complete specification and therefore overlook whether an agent knows when clarification is needed before modeling. We introduce OR-Clarify, a benchmark for pre-formulation clarification. Each task presents a partial public problem description, withholds structured hidden slots, and evaluates agents through bounded interaction with a simulated user. The benchmark supports both openended and choice-based clarification, and measures slot recovery, stopping behavior, silent assumptions, and interaction cost. We further propose Interactive Optimization (InterOPT), a two-stage framework that identifies unresolved formulation-critical gaps and uses them to guide whether to ask the next question or to stop. In our choice-based experiments, InterOPT substantially outperforms all baselines in exact slot recovery; in the open-ended setting, it remains competitive with strong prior methods. Together, OR-Clarify and InterOPT reframe OR assistance as a selective completeness decision: clarify when needed, stop when ready, and quantify what remains missing.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.05258",
  "author_total": 6,
  "authors": [
    {
      "name": "Sihan Ge"
    },
    {
      "name": "Yichen Lin"
    },
    {
      "name": "Chenyu Zhou"
    },
    {
      "name": "Jianghao Lin"
    },
    {
      "name": "Tao Yao"
    },
    {
      "name": "Dongdong Ge"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/20z3r7vczd28d.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05258",
  "fulltext_url": null,
  "id": "20z3r7vczd28d",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.05258",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "1702|Topic Modeling"
    ]
  ],
  "openalex": "W7211881297",
  "pdf_url": "https://arxiv.org/pdf/2609.05258",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/20z3r7vczd28d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "2609.05258",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05258"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization",
  "title_text": "Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:58Z",
  "url": "https://research.draconitic.com/paper/20z3r7vczd28d",
  "venue": "arXiv",
  "versions": []
}