Core Viewpoint - The article discusses the development of the BioMARS system, which integrates natural language processing, computer vision, and modular robotics to automate biological experiments, overcoming limitations of traditional automation tools [1][21]. Group 1: BioMARS System Overview - BioMARS addresses common laboratory pain points such as literature review fatigue, execution errors from traditional robots, and difficulties in optimizing experimental parameters [2][3]. - The system consists of three core components: Biologist Agent, Technician Agent, and Inspector Agent, each with distinct roles in the experimental process [3]. Group 2: Biologist Agent - The Biologist Agent acts as the planner, automatically retrieving and understanding relevant literature to generate experimental plans based on laboratory resource constraints [6][3]. Group 3: Technician Agent - The Technician Agent serves as the executor, converting natural language experimental plans into executable instructions for robots, ensuring logical coherence and practical execution [7][3]. Group 4: Inspector Agent - The Inspector Agent functions as the supervisor, utilizing computer vision for multi-stage perception and error detection, enhancing reliability and safety by identifying operational deviations [10][3]. Group 5: Experimental Results - In comparative experiments with HeLa, Y79, and DC2.4 cell lines, BioMARS demonstrated significant advantages, achieving similar cell survival rates and morphology integrity as manual operations while improving reproducibility [11][12]. - Automation reduced the manual operation time for cell line passage from 60 minutes to 5-8 minutes, validating the system's feasibility and efficiency [12]. Group 6: Optimization Capabilities - Beyond executing preset experimental plans, BioMARS possesses advanced biological optimization capabilities, adjusting growth factor concentrations and culture times based on previous failures to enhance differentiation efficiency [17][18]. - The system employs decision-making strategies that incorporate biological knowledge, reducing reliance on manual tuning and providing scalable solutions for complex biological systems [18]. Group 7: Conclusion and Future Outlook - BioMARS represents a new generation of automated experimentation, integrating reasoning capabilities and marking a shift towards AI-native approaches in life sciences, potentially transforming high-throughput cell culture and drug screening processes [21].