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arXiv AI By Sihan Ge, Yichen Lin, Chenyu Zhou, Jianghao Lin, Tao Yao, Dongdong Ge

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

Read the original on arXiv AI →

The paper introduces OR‑Clarify, a benchmark that tests whether large language models can identify missing elements in natural‑language optimization requests before formulating a mathematical model. Each task provides a partial problem description and hides structured slots; agents interact with a simulated user to recover these slots, with metrics for accuracy, stopping decisions, and interaction cost. The authors also propose InterOPT, a two‑stage framework that detects unresolved gaps and decides whether to ask further questions or stop, achieving superior slot recovery in choice‑based experiments and competitive performance in open‑ended settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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