用多智能体协作自动生成并验证约束模型，提升自然语言转代码的准确率。
CP-SynC: Multi-Agent Zero-Shot Constraint Modeling in MiniZinc with Synthesized Checkers
- 多智能体协同生成与优化约束模型，通过合成检查器验证语义正确性。
- 在100个基准问题上，零样本建模准确率显著优于现有方法。
- 适合需要自动化求解组合优化问题的研究者与工程师使用。
约束规划（CP）是求解组合问题的强大范式，但将自然语言问题描述转化为可执行模型仍是重大瓶颈。尽管大语言模型（LLMs）在自动化翻译方面展现出潜力，但在测试时缺乏参考答案的情况下，常因细微语义错误而失效。为此，我们提出CP-SynC（基于合成检查器的约束规划建模），一种用于MiniZinc的零样本约束建模多智能体工作流。CP-SynC协调建模智能体生成并迭代优化候选模型，以及验证智能体通过合成语义检查器提供语义正确性反馈。为降低单个LLM输出的噪声影响，该方法并行探索多个建模路径，并由选择智能体通过多智能体证据聚合选出最终模型。在包含100个CP问题的基准测试中，CP-SynC显著优于现有基线方法。
原文摘要 · Abstract (English)
Constraint Programming (CP) is a powerful paradigm for solving combinatorial problems, yet translating natural language problem descriptions into executable models remains a significant bottleneck. While Large Language Models (LLMs) show promise in automating this translation, they often struggle with subtle semantic errors in the absence of oracle validation at test time. To address this, we introduce CP-SynC (Constraint Programming modeling with Synthesized Checkers), a multi-agent workflow for zero-shot constraint modeling in MiniZinc. CP-SynC coordinates modeling agents that generate and refine candidate models and validation agents that synthesize semantic checkers to provide feedback on semantic correctness. To mitigate noise inherent in individual LLM outputs, CP-SynC explores multiple modeling trajectories in parallel and employs selection agents to select the final model via multi-agent evidence aggregation. Extensive experiments on a benchmark of 100 CP problems show that CP-SynC substantially outperforms existing baselines in MiniZinc modeling.
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