2025/09/10 by Junyang Cai, Cai, Junyang, Serdar Kadıoğlu +3
Computer Science · #Class (philosophy) #Constraint (computer-aided design) #Constraint Satisfaction and Optimization #Constraint logic programming #Constraint programming #Multimodal Machine Learning Applications #Process (computing) #Task (project management) #Topic Modeling #Translation (biology)
paper · pdf · doi:10.48550/arxiv.2509.08970
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Natural language descriptions of optimization or satisfaction problems are challenging to translate into correct MiniZinc models, as this process demands both logical reasoning and constraint programming expertise. We introduce Gala, a framework that addresses this challenge with a global agentic approach: multiple specialized large language model (LLM) agents decompose the modeling task by global constraint type. Each agent is dedicated to detecting and generating code for a specific class of global constraint, while a final assembler agent integrates these constraint snippets into a complete MiniZinc model. By dividing the problem into smaller, well-defined sub-tasks, each LLM handles a simpler reasoning challenge, potentially reducing overall complexity. We conduct initial experiments with several LLMs and show better performance against baselines such as one-shot prompting and chain-of-thought prompting. Finally, we outline a comprehensive roadmap for future work, highlighting potential enhancements and directions for improvement.