Newest papers

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

Japanese

現実世界のコード決断の問題は2種類の制約を組み合わせています:満たされる必要がある要件は、ハード制約と呼ばれ、破られる可能性のある好みは、ソフト制約と呼ばれています。これらの問題は、しばしば自然言語で意思決定者に提示されるため、大規模な言語モデルは、その解決策を助けるための自然なツールを提供しています。これは、自然言語の決定問題の記述を実行可能な数学的最適化モデルに翻訳するためにLLMsを使用することに焦点を当てた自動構成を生み出しました。しかし、この研究は、多くの実用的な問題が明示的にそれらをモデルする必要があるという事実にもかかわらず、消費者、マネージャー、および計画者によって提案されたような

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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