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ASTOR: Benchmarking and Improving Autoformulation for Soft-Constraint Optimization with a Formal Grammar

Amer Goel, Yeganeh Alimohammadi, Andrew Daw · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26819 · cited by 0 works (OpenAlex) · open-access PDF

Arabic

وتشمل هذه التحديات عدة أنواع مختلفة من التهديدات: التحديات التي يجب أن تتحلى بها، والتي تسمى التهديدات الصارمة، والتحديات التي يمكن أن تتحلى بها، والتي تسمى التهديدات الصارمة. لأن هذه التحديات غالباً ما يتم تقديمها للمتدخلين في لغة الطبيعية، وتقديم نموذج لغة كبيرة أدوات طبيعية لمساعدتهم في حلها. هذا ما أدى إلى خلق نموذج تلقائي يهدف إلى استخدام LLM لترجمة وصفات لغة الطبيعية للمشكلات في التغيير إلى نموذج تحسين الرياضيات المعمول به. ومع ذلك، فإن هذه الدراسة غالباً ما تجاهلت التحديات الصارمة مثل تلك التي تم تقديمها من قبل المستهلكين والمديرين والمختص

Machine translation (Meta M2M100); the English original follows.

Abstract

Real-world decision problems combine two qualitatively different kinds of constraints: requirements that must be satisfied, called hard constraints, and preferences that can be violated, called soft constraints. Because these problems are often posed to decision-makers in natural language, large language models offer a natural tool for assisting with their solution. This has given rise to autoformulation, which focuses on using LLMs to translate natural-language descriptions of decision problems into executable mathematical optimization models. However, this research often neglects soft constraints like those proposed by consumers, managers, and planners, despite the fact that many practical problems require modeling them explicitly. We introduce ASTOR, an inference-time framework that maps a natural-language decision problem to a grammar-guided abstract syntax tree (AST) before deterministically compiling the resulting representation into executable optimization code. We also introduce SoftOR, a unified benchmark of natural language problems which focuses on soft constraint formulations. On SoftOR, ASTOR achieves the best aggregate accuracy among comparable single-request and multi-agent LLM methods while remaining competitive on standard autoformulation benchmarks that include only hard constraints. Code and data are available at https://anonymous.4open.science/r/astor-softor-iclr-E34C/README.md.

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Topics: Computer science, Executable, Benchmark (surveying), Programming language, Syntax, Natural language

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