To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for adaptive LLM-generated fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce Verbal Adversarial Feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. Experiments on an LLM-generated fake news benchmark show that RADAR outperforms retrieval-augmented trainable baselines and general-purpose LLMs with retrieval. Further analysis shows that retrieval on both the generator and detector sides improves performance, while VAF and few-shot demonstrations offer complementary benefits. RADAR also transfers better to fake news generated by an unseen external attacker, suggesting improved generalization beyond the specific co-evolved generator used during training.

To enhance large language models (LLMs) with real-world task-solving capabilities, integrating workflow knowledge into LLMs has emerged as a promising direction. However, real-world conversations are inherently dynamic—users often shift intents or request actions beyond the scope of the current workflow. Existing systems struggle to detect such transitions and to decide when to retrieve or switch to a new workflow. This paper presents FlowSwitch, a state-aware framework that learns when to search for relevant workflows and switch between them during multi-turn dialogues. A policy module determines whether to continue within the current workflow or transition to a new one based on contextual representations. When searching, a retriever identifies the most relevant workflow knowledge given the dialogue state. We conduct comprehensive experiments to explore the optimal configuration of FlowSwitch, including workflow format, retrieval input type, and retrieval method. Experimental results show that our framework, when using the agent’s self-generated search queries, achieves the highest Top-1 accuracy and Mean Average Precision (MAP). Moreover, FlowSwitch reduces nearly 50% of search operations, substantially lowering computational cost and response time.