=================== TEXTRA-IA Research =================== Context ---------------------- The world of research is evolving quickly, and we need tools to make reading articles and staying up to date easier. Our focus is on reinforcement learning scientific papers. The goal is to provide a solution that synthesizes scientific papers without skipping any information, allowing users to interact with them by asking questions about concepts that might not be within their knowledge. By incorporating knowledge graphs, we aim to make information more accessible and visual, while taking into account user learning preferences. Challenges We Have to Face ------------------------------ **Layout and Natural Complexity of RL Papers:** While it is easy for humans to recognize layout and connections—distinguishing tables from figures, images, titles, and normal text—for a machine, this is not as straightforward. There are many tools available that we just need to orchestrate to compensate for the gaps between them. For equations, we will need to train our own model. **Knowledge of Reinforcement Learning:** Each scientific article brings new truths and discoveries that we need to organize into an easily accessible and navigable format, highlighting the relationships between concepts. We must also keep up to date with what's happening and adapt to the growing volume of publications, which can lead to information overload. **Variable User Learning Preferences:** What pleases one user may not please another, so we must accommodate varying learning styles. Objectives ---------------------- Our primary objectives are to enhance the accessibility of reinforcement learning research, streamline the synthesis of scientific papers, and empower users to engage deeply with the material. We aim to create a user-friendly solution that fosters curiosity and exploration. Technological Framework ---------------------- A combination of existing tools and technologies alongside custom-developed solutions, including LLMs, VLMS, NLP, and OCR. Future Directions ---------------------- As the field of reinforcement learning continues to evolve, so too will our solution with continuous improvement and adaptation. The upcoming versions will expand to include additional fields within artificial intelligence, broadening its applicability beyond reinforcement learning. Currently, this initiative serves as a proof of concept, allowing us to test our ideas and gather valuable feedback for future enhancements. Our solution --------------- Our solution aims to address these specific challenges mentioned above. Details of how that's done are provided in the rest of the documentation.