Formalizing complex reasoning from natural text is one of the central challenges in computational linguistics. It requires systems to understand not just keywords but also the context and complex reasoning embedded in a text. Current Argument Mining (AM) techniques identify basic claims and premises, yet they often struggle to capture the richer structural information required by advanced schemas such as the Carneades Argumentation Framework (CAF), which incorporates features such as premise types, proof standards, and argument schemes. We address this limitation by introducing CAF-Gen, an automated multi-agent framework designed to enrich shallow argument structures into CAF-compliant argument models. By employing an iterative Creator-Reviewer pipeline, a creator agent's output is validated by a critical agent to ensure structural integrity. This multi-agent collaboration is crucial for mitigating the structural instability typical of single-pass generative models. Our experiments demonstrate that the iterative feedback loop improves the quality of the resulting data and achieves strong alignment with the original annotations, while producing structurally richer models. Our findings show that the multi-agent system can overcome the limitations of single-pass generation, providing a robust methodology for the automated modeling of formal argumentation.

翻译：将自然文本中的复杂推理形式化是计算语言学的核心挑战之一。这要求系统不仅要理解关键词，还要理解文本中蕴含的语境与复杂推理。当前的论据挖掘技术能够识别基本主张和前提，但往往难以捕捉高级模式（如引入前提类型、证明标准与论证方案的Carneades论证框架）所需的更丰富的结构信息。为解决这一局限，我们提出了CAF-Gen——一种旨在将浅层论证结构丰富为符合CAF规范的论证模型的自动化多智能体框架。通过采用迭代的创建者-审阅者流水线，创建智能体的输出经关键智能体验证以确保结构完整性。这种多智能体协作对于缓解单次生成模型典型的结构不稳定性至关重要。实验表明，迭代反馈循环提升了最终数据的质量，在生成结构更丰富的模型的同时，与原标注实现了高度对齐。我们的研究结果表明，多智能体系统能够克服单次生成的局限性，为自动化建模形式论证提供了稳健的方法论。