--The widespread dissemination of fake news on social media has significantly impacted society, resulting in serious consequences. Conventional deep learning methodologies employing small language models (SLMs) suffer from extensive supervised training requirements and difficulties adapting to evolving news environments due to data scarcity and distribution shifts. EFND) framework to address these challenges. We further introduce a lifelong knowledge editing module based on a Mixture-of-Experts architecture to incrementally update LLMs and a replay-based continue learning method to ensure SLMs retain prior knowledge without retraining entirely. EFND significantly outperforms existed methods, effectively improving detection accuracy and adaptability in continuous emergent fake news scenarios. HE rampant spread of fake news on the Internet has already caused significant societal impact [1]. For instance, the spread of fake news during the Covid-19 pandemic has led to harmful consequences such as drug misuse and incorrect treatment methods [2]. As illustrated in Figure 2(a), fake news on emergent events evolves continuously, presenting a challenge for real-time detection systems to keep pace with its evolution. Furthermore, an alarming pattern known as "rumor resurgence" frequently occurs in social media, wherein past misinformation reappears, perpetuating its societal impact [3]. Chaozhuo Li is with School of Cyber Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: lichaozhuo@bupt.edu.cn).

An abduction-based diagnosis technique for a class of discrete-event systems (DESs), called deep DESs (DDESs), is presented. A DDES has a tree structure, where each node is a network of communicating automata, called an active unit (AU). The interaction of components within an AU gives rise to emergent events. An emergent event occurs when specific components collectively perform a sequence of transitions matching a given regular language. Any event emerging in an AU triggers the transition of a component in its parent AU. We say that the DDES has a deep behavior, in the sense that the behavior of an AU is governed not only by the events exchanged by the components within the AU but also by the events emerging from child AUs. Deep behavior characterizes not only living beings, including humans, but also artifacts, such as robots that operate in contexts at varying abstraction levels. Surprisingly, experimental results indicate that the hierarchical complexity of the system translates into a decreased computational complexity of the diagnosis task. Hence, the diagnosis technique is shown to be (formally) correct as well as (empirically) efficient.