The field of fact-checking and document parsing is moving towards more robust and scalable solutions. Researchers are exploring new methods to improve the accuracy and reliability of fact-checking models, including the use of large language models and hierarchical segmentation techniques. A key challenge in this area is the vulnerability of fact-checking systems to adversarial attacks, which can manipulate or generate false claims. To address this, researchers are developing adversary-aware defenses and evaluating the resilience of current models. Another important aspect is the need for equitable access to trustworthy fact-checking solutions, particularly in non-English languages and for resource-constrained organizations. Noteworthy papers in this area include: Scaling Truth: The Confidence Paradox in AI Fact-Checking, which reveals a concerning pattern of smaller models showing high confidence despite lower accuracy. Adversarial Attacks Against Automated Fact-Checking: A Survey, which provides a comprehensive overview of key challenges and recent advancements in adversary-aware defenses.