The field of research is witnessing a significant shift towards AI-driven automation and human-AI collaboration. Recent developments have focused on leveraging large language models (LLMs) and other AI technologies to accelerate various aspects of the research process, including evidence extraction, document analysis, and peer review. These advancements aim to enhance the efficiency, accuracy, and accessibility of research, while also exploring new paradigms for scientific discovery and knowledge generation. Noteworthy papers in this area have demonstrated the potential of AI-powered systems to automate tasks such as literature review, data analysis, and manuscript preparation, as well as to facilitate human-AI collaboration in research workflows. For instance, the introduction of AI margin notes in document reader software has shown promise in improving the reading experience and facilitating more efficient document analysis. Furthermore, the development of fully autonomous research agents, such as OpenLens AI, has the potential to revolutionize the research process by automating the entire pipeline from literature review to manuscript preparation. However, concerns regarding bias and fairness in AI-driven peer review have also been raised, highlighting the need for careful evaluation and validation of these systems. Overall, the field is moving towards a future where AI-driven automation and human-AI collaboration play an increasingly important role in advancing research and scientific discovery. Notable papers include: Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement, which proposes an AI-assisted framework for automating evidence extraction from large-scale textual data. AEGIS: An Agent for Extraction and Geographic Identification in Scholarly Proceedings, which presents a novel system for automated extraction and identification of papers from specific geographic regions.