Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
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 benchmar...
| Authors | Sihan Ge, Yichen Lin, Chenyu Zhou et al. |
|---|---|
| Primary category | math.OC |
| Published | 4 September 2026 |
| Last updated at source | 8 September 2026 |
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