Core Viewpoint - The article discusses the groundbreaking announcement by Stanford University regarding the Agents4Science 2025 conference, which will allow AI to be recognized as the first author of research papers, marking a significant shift in the academic landscape [2][3][4][5]. Group 1: Conference Overview - Agents4Science 2025 will be held online on October 22, 2025, coinciding with ICCV 2025 [12][13][19]. - The conference aims to explore the role of AI in scientific research, focusing on transparency, accountability, and the establishment of standards for AI contributions [14][18]. Group 2: Submission Guidelines - The primary requirement for submissions is that the first author must be an AI system, which will lead the hypothesis generation, experimentation, and writing processes [5][6]. - Human researchers can participate as co-authors, primarily in a supportive or supervisory role, with a limit of four submissions per human author [6][19]. Group 3: Review Process - The review process will involve multiple AI systems conducting initial evaluations to mitigate bias, followed by a human expert committee for final assessments [9][14]. - All submitted papers and reviews will be made publicly available to foster transparency and allow for the study of AI's strengths and weaknesses in research [14][18]. Group 4: Community Response - The announcement has generated excitement and interest among researchers, with many expressing eagerness to submit papers and explore the implications of AI as a first author [15][16].

Core Viewpoint - The article discusses a breakthrough in potential treatment for dry age-related macular degeneration (dAMD) discovered by an AI-driven research team, Future House, which utilized a multi-agent system to identify the ROCK inhibitor Ripasudil as a promising candidate for treatment [3][4][10]. Group 1: AI-Driven Research Process - The entire research process was primarily driven by AI, with human researchers only conducting laboratory experiments and writing the final paper [2][7]. - The multi-agent system named "Robin" integrated three agents (Crow, Falcon, Finch) to automate key steps in scientific discovery, including hypothesis generation, experimental design, and data analysis [18][19]. - The research was completed in approximately 10 weeks, significantly faster than traditional methods [9]. Group 2: Discovery and Validation - Robin identified Ripasudil, an existing drug approved for glaucoma treatment in Japan, as having potential effects on dAMD [4][50]. - The team consulted multiple experts in the field, who recognized the innovation and value of this discovery [5]. - Initial experiments showed that Ripasudil could enhance RPE cell phagocytic activity by 7.5 times compared to the control group, indicating its potential effectiveness [50]. Group 3: Mechanistic Insights - The research revealed that the ROCK inhibitor Y-27632 could restore RPE cell phagocytic efficiency, supporting the hypothesis generated by Robin [31]. - The analysis indicated that the lipid efflux pump ABCA1 was upregulated threefold in Y-27632 treated cells, which is significant for maintaining RPE cell health [45][47]. - The findings suggest that AI-driven research can not only identify effective therapeutic compounds but also uncover new molecular targets in disease pathways [48]. Group 4: Future Directions - Future House plans to open-source the code and data from this research next week, promoting transparency and collaboration in scientific research [12]. - The team emphasizes that further studies, including human trials, are necessary before clinical application can be considered [51].