Core Insights - The article discusses the challenges researchers face in keeping up with the rapid advancements in AI research, highlighting the need for automated systems to assist in literature review and trend tracking [1][2]. Group 1: Research Overview - A new system called Real Deep Research (RDR) has been developed to automatically conduct high-quality literature reviews and track trends in AI and robotics [5][8]. - RDR collects thousands of papers from top conferences, filters them based on prompts, and compresses each paper into structured summaries [5][8]. Group 2: System Functionality - The system records various aspects of foundational AI models and robotics, including data sources, model mechanisms, outputs, learning objectives, and training methods [7][8]. - RDR enables automatic generation of literature reviews, visualizes trends over time, and facilitates semantic searches for newcomers in the field [8][13]. Group 3: Methodology - The RDR pipeline consists of four main components: data preparation, content reasoning, content projection, and embedding analysis [17]. - Data preparation involves collecting papers from top conferences and filtering them for relevance using efficient large language models (LLMs) [18][19]. Group 4: Analysis and Results - RDR's performance was evaluated through user studies, showing it outperformed baseline methods in various domains, including natural language processing and robotics [28][29]. - The system achieved an average ranking of 1.30, indicating its effectiveness in generating high-quality reviews compared to other models [28][29]. Group 5: Future Implications - The authors aim for RDR to assist researchers in AI and robotics in identifying unexplored intersections between different fields and recognizing emerging research opportunities [13][18].