让不懂编程的人也能快速生成神经符号程序。
An Agentic Framework for Neuro-Symbolic Programming
- 用智能流程自动把自然语言任务转成完整代码
- 非专业人士10-15分钟完成原本需数小时的工作
- 支持人工介入优化，适合初学者和专家
将符号约束融入深度学习模型可提升其鲁棒性、可解释性和数据效率，但实现过程耗时且困难。现有框架如 DomiKnowS 通过声明式编程接口简化集成，但仍要求用户掌握特定语法。我们提出 AgenticDomiKnowS (ADS)，通过智能工作流将自由形式的任务描述转化为完整的 DomiKnowS 程序，该流程分步创建并测试每个组件。工作流支持人工介入，使熟悉 DomiKnowS 的用户可优化中间结果。实验表明，ADS 能让经验用户与新手在 10-15 分钟内快速构建神经符号程序，开发时间从数小时大幅缩短。
原文摘要 · Abstract (English)
Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient. Still, it remains a time-consuming and challenging task. Existing frameworks like DomiKnowS help this integration by providing a high-level declarative programming interface, but they still assume the user is proficient with the library's specific syntax. We propose AgenticDomiKnowS (ADS) to eliminate this dependency. ADS translates free-form task descriptions into a complete DomiKnowS program using an agentic workflow that creates and tests each DomiKnowS component separately. The workflow supports optional human-in-the-loop intervention, enabling users familiar with DomiKnowS to refine intermediate outputs. We show how ADS enables experienced DomiKnowS users and non-users to rapidly construct neuro-symbolic programs, reducing development time from hours to 10-15 minutes.
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