标题： CP代理：代理约束编程 显示英文标题

标题： CP-Agent: Agentic Constraint Programming

摘要： 将自然语言问题描述翻译成形式化约束模型仍然是约束编程中的一个基本挑战，这需要对问题领域和建模框架都有深入的专业知识。 以往自动完成此翻译的方法采用了固定的工作流程和预定的建模步骤，在大量基准问题上都出现了失败。 我们提出了一种新的方法，采用纯粹的代理策略，没有任何固定的流水线。 我们开发了一个基于ReAct（思考与行动）原则的通用Python编码代理，利用持久化的IPython内核进行有状态的代码执行和迭代开发。 而不是将约束编程逻辑嵌入到代理架构中，领域专业知识仅通过精心设计的项目提示注入。 该代理将这种提示编码的知识与对文件操作和代码执行工具的访问相结合，使其能够动态地测试假设、调试失败并验证解决方案。 该架构仅用几百行代码实现，成功解决了CP-Bench约束编程基准集中的所有101个问题。 结果表明，约束建模任务需要结合通用的编码工具和通过提示编码的领域专业知识，而不是专门的代理架构或预定义的工作流程。 显示英文摘要

摘要： Translating natural language problem descriptions into formal constraint models remains a fundamental challenge in constraint programming, requiring deep expertise in both the problem domain and modeling frameworks. Previous approaches to automating this translation have employed fixed workflows with predetermined modeling steps, failing on a significant number of benchmark problems. We present a new approach using a pure agentic strategy without any fixed pipeline. We developed a general-purpose Python coding agent based on the ReAct (Reason and Act) principle, utilizing a persistent IPython kernel for stateful code execution and iterative development. Rather than embedding constraint programming logic into the agent architecture, domain-specific expertise is injected solely through a carefully crafted project prompt. The agent combines this prompt-encoded knowledge with access to file operations and code execution tools, enabling it to test hypotheses, debug failures, and verify solutions dynamically. Implemented in just a few hundred lines of code, this architecture successfully solves all 101 problems of the CP-Bench constraint programming benchmark set. The results suggest that constraint modeling tasks require the combination of general coding tools and domain expertise encoded in prompts, rather than specialized agent architectures or predefined workflows.