The field of legal intelligence and reasoning is rapidly evolving, with a focus on developing innovative solutions to improve the efficiency and accuracy of legal services. Recent research has explored the application of large language models, knowledge graphs, and agentic reasoning frameworks to enhance legal judgment prediction, claim generation, and court simulation. These advancements have the potential to transform the legal landscape, enabling more transparent and accountable decision-making processes. Notably, the development of legal knowledge graphs and ontologies has improved the ability to capture and represent complex legal reasoning, while the integration of AI-powered systems with legal databases has enhanced the accessibility and efficacy of legal consultation services. Some noteworthy papers in this area include: GLARE, which introduces an agentic legal reasoning framework that dynamically acquires key legal knowledge to improve the breadth and depth of reasoning. Capturing Legal Reasoning Paths from Facts to Law in Court Judgments using Knowledge Graphs, which constructs a legal knowledge graph to capture the full structure of legal reasoning as it appears in real court decisions. AI-Powered Legal Intelligence System Architecture, which proposes a comprehensive framework for automated legal consultation and analysis, integrating advanced AI, natural language processing, and federated legal databases.