The analysis and interpretation of legal case scenarios remain complex due to their contextual, multi-perspective, and knowledge-intensive nature. Existing systems primarily provide rule-based outputs, keyword-based search, and static legal information retrieval tools that fail to support dynamic reasoning or contextual understanding. To overcome these limitations, this project proposes LexAI — an intelligent legal reasoning platform that leverages a multi-agent architecture combined with Retrieval-Augmented Generation (RAG) to simulate courtroom-style decision-making. The system interprets user-provided case inputs, retrieves relevant legal knowledge such as IPC sections using semantic similarity, and generates structured arguments from opposing perspectives before producing an explainable verdict. It incorporates multi-round reasoning, argument evaluation, and legal evidence visualization to enhance transparency and interpretability. By providing an interactive and scalable platform, LexAI improves accessibility, reduces manual effort, and bridges the gap between complex legal information and practical decision-making.
S et al. (Wed,) studied this question.