发表机构
University of North Texas; Texas Academy of Mathematics and Science (TAMS)（北得克萨斯大学; 德克萨斯数学与科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对CLEF 2026 CheckThat!实验室任务3，提出多智能体管道系统，结合证据检索、结构化规划、文章生成、自我批判及引用审核等方法，强调证据选择、结构化生成与生成后引用验证对事实核查文章生成的重要性。
AI 中文摘要
本文介绍了我们用于CLEF 2026 CheckThat!实验室任务3的系统，该任务专注于根据声明、真实性标签和证据文档生成完整的事实核查文章。我们提出了一个多智能体管道，它结合了证据检索、结构化事实规划、文章生成、门控自我批判和基于自然语言推理的引用审核。该系统使用密集检索、重新排序和源平衡选择来检索与声明相关的证据，然后从结构化计划生成一篇有引用支持的文章。门控自我批判阶段修正基础薄弱的草稿，而自然语言推理引用审核器修复缺失的引用并删除无支持或冗余的引用。该方法强调了结合证据选择、结构化生成和生成后引用验证对于基于源的事实核查文章生成的重要性。
英文摘要
This paper presents our system for Task 3 of the CLEF 2026 CheckThat! Lab, which focuses on generating full fact-checking articles from claims, veracity labels, and evidence documents. We propose a multi-agent pipeline that combines evidence retrieval, structured fact planning, article generation, gated self-critique, and NLI-based citation auditing. The system retrieves claim-relevant evidence using dense retrieval, reranking, and source-balanced selection, then generates a citation-supported article from a structured plan. A gated self-critique stage revises weakly grounded drafts, while the NLI citation auditor repairs missing citations and removes unsupported or redundant ones. The approach highlights the importance of combining evidence selection, structured generation, and post-generation citation validation for source-grounded fact-checking article generation.
CommentsCLEF 2026 Working Notes / CheckThat! Lab at CLEF 2026, Jena, Germany
