Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

2026-09-04

AI Summary

研究解决了优化模型中因问题描述不完整导致的挑战，通过引入OR-Clarify基准和Interactive Optimization框架来识别并填补关键信息缺口。

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.

Problem

• Large language models
• Optimization models
• Clarification
• Incomplete specifications
• Interactive Optimization

Innovation

• pre-formulation clarification
• Interactive Optimization
• OR-Clarify
• selective completeness decision