Delineate & Decipher: A RAG-Powered AI Platform for Research Paper Analysis and Visual Math Problem Solving

The exponential growth in the research papers and complex mathematical problems has posed serious challenges in information retrieval and its review for analysis. This paper presents “Delineate and Decipher,” a platform powered by AI using Retrieval Augmented Generation models to solve these challenges. The platform will combine natural language processing with machine learning techniques to provide context based answers from research papers and also solve visual math problems through its interactive interface. A hybrid system was based on the Gemini model of visual problem solving in mathematics, whereas the retrieval models were powered with GROQ. The platform embeds both documents and mathematical equations into vector spaces, hence guaranteeing efficiency in document searching and retrieval. Precisely, all this necessary information has kept in mind while making the system function over huge datasets of research papers by splitting documents into smaller chunks with strong results based on accurate/context retrieval. It also allows the user to write mathematical equations visually and get solutions to them using real time API with Gemini Flash, hence not just a tool for doing research analysis but also educational purposes. This work saves time for the researchers and students who search for some information and solutions of cumbersome mathematics problems. The proposed platform is powered by state of the art AI techniques: FAISS for vector storage and Google Generative AI embeddings for document representation. It provides better access, precision, and interactivity to academic research and education. Future versions might involve even better language models and more comprehensive datasets, which have the interactions between users and scholarly materials and mathematical tools