Cross-domain misinformation detection is challenging, as misinformation arises across domains with substantial differences in knowledge and discourse. Existing methods often rely on single-perspective cues and struggle to generalize to challenging or underrepresented domains, while reasoning large language models (LLMs), though effective on complex tasks, are limited to same-distribution data. To address these gaps, we introduce RAAR, the first retrieval-augmented agentic reasoning framework for cross-domain misinformation detection. To enable cross-domain transfer beyond same-distribution assumptions, RAAR retrieves multi-perspective source-domain evidence aligned with each target sample's semantics, sentiment, and writing style. To overcome single-perspective modeling and missing systematic reasoning, RAAR constructs verifiable multi-step reasoning paths through specialized multi-agent collaboration, where perspective-specific agents produce complementary analyses and a summary agent integrates them under verifier guidance. RAAR further applies supervised fine-tuning and reinforcement learning to train a single multi-task verifier to enhance verification and reasoning capabilities. Based on RAAR, we trained the RAAR-8b and RAAR-14b models. Evaluation on three cross-domain misinformation detection tasks shows that RAAR substantially enhances the capabilities of the base models and outperforms other cross-domain methods, advanced LLMs, and LLM-based adaptation approaches. The project will be released at https://github.com/lzw108/RAAR.

翻译：跨领域虚假信息检测面临严峻挑战，因为虚假信息常出现在知识体系和话语方式差异显著的领域之间。现有方法通常依赖单一视角线索，难以泛化至具有挑战性或代表性不足的领域；而基于推理的大语言模型（LLMs）虽然在复杂任务上表现优异，却受限于同分布数据假设。为弥补这些不足，我们提出了RAAR——首个面向跨领域虚假信息检测的检索增强型智能推理框架。为实现超越同分布假设的跨领域迁移，RAAR通过检索与目标样本在语义、情感和写作风格上对齐的多视角源领域证据。为克服单一视角建模与系统性推理缺失的问题，RAAR通过专业化的多智能体协作构建可验证的多步推理路径：领域特定智能体生成互补性分析，汇总智能体在验证器指导下进行整合。RAAR进一步采用监督微调与强化学习训练统一的多任务验证器，以提升验证与推理能力。基于该框架，我们训练了RAAR-8b和RAAR-14b模型。在三个跨领域虚假信息检测任务上的评估表明，RAAR显著增强了基础模型的能力，其性能优于现有跨领域方法、先进大语言模型及基于大语言模型的适配方法。项目代码将于https://github.com/lzw108/RAAR发布。