The field of natural language processing is witnessing a significant shift towards leveraging large language models (LLMs) for improving information retrieval and fact-checking capabilities. Recent developments indicate a growing focus on enhancing the efficiency and effectiveness of LLMs in accessing and verifying web content. There is an increasing emphasis on creating AI-native architectures that facilitate semantic retrieval, reducing the complexity and inefficiencies associated with traditional document-centric approaches. Furthermore, the importance of curated context in reliable fact-checking is being underscored, with studies highlighting the benefits of integrating high-quality, domain-specific data to improve the accuracy of LLMs. The development of novel frameworks and methodologies, such as those incorporating contrastive learning and self-refining explanatory models, is also noteworthy. These advancements have the potential to revolutionize the way we interact with and trust online information. Notable papers include Toward an AI-Native Internet, which introduces a groundbreaking web architecture optimized for AI-driven semantic retrieval, and Large Language Models Require Curated Context for Reliable Political Fact-Checking, which demonstrates the significance of high-quality context in fact-checking. Additionally, FISCAL and REFLEX present innovative approaches to financial fact-checking and self-refining explainable fact-checking, respectively.