Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
September 7, 2026: The InterOPT framework and OR-Clarify dataset were published on arXiv and Hugging Face…
2026-09-07 12:00Science🔥 47.2 heat score
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On September 7, 2026, researchers released the interactive optimization open-source framework called InterOPT and the OR-Clarify benchmark dataset. This framework aims to address the bias issues caused by information gaps when large language models convert natural language descriptions into mathematical optimization models. InterOPT uses a two-stage dynamic clarification mechanism to identify key gaps in formulas, guiding the model to ask questions or stop the interaction at appropriate times. Experiments show that this method is significantly better than existing baselines in accurately restoring missing information and remains competitive with the strongest methods in open-ended settings. The framework supports iterative clarification of problems by large language models, refinement of formulas, and verification of generated solutions through solver feedback, transforming operations research assistance into按需 clarification and selective completeness decision-making.
September 7, 2026: The InterOPT framework and OR-Clarify dataset were published on arXiv and Hugging Face…
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2026-09-07
The InterOPT framework and OR-Clarify dataset were released.
arXiv and Hugging Face reported that researchers have released the InterOPT interactive optimization open-source framework and the OR-Clarify benchmark dataset, aiming to address the information loss bias problem in natural language-to-mathematical optimization models by large language models.
The researchers released the InterOpt open-source framework, aiming to improve the interaction between large language models and optimization solvers through the “solver in loop” paradigm. The framework includes the OR-Clarify dataset for studying fuzzy solutions and its corresponding open-source implementation. InterOpt enables large language models to iteratively clarify optimization problems, refine formulas, and verify generated solutions through optimization feedback.