The field of automated scholarly content generation and analysis is witnessing significant advancements, driven by innovations in artificial intelligence, natural language processing, and machine learning. Researchers are developing novel systems and frameworks that can generate high-quality academic surveys, system architecture diagrams, and scientific posters, as well as analyze and understand the structure and content of these artifacts. Furthermore, there is a growing focus on addressing the disparities and biases in data science and artificial intelligence, particularly in terms of gender and diversity. Noteworthy papers in this area include ARISE, which introduces an agentic rubric-guided iterative survey engine for automated generation and refinement of academic survey papers, and RhinoInsight, which presents a deep research framework that enhances robustness, traceability, and overall quality of model behavior and context. Other notable works include Paper2SysArch, which establishes a foundational benchmark for automated scientific visualization, and DR Tulu, which develops a reinforcement learning approach for deep research with evolving rubrics. These advancements have the potential to revolutionize the way researchers work and collaborate, and to promote greater diversity, equity, and inclusion in the field.