Core Viewpoint - The article discusses the innovative framework DrSR (Dual Reasoning Symbolic Regression) developed by researchers at the Institute of Automation, Chinese Academy of Sciences, which enables large models to analyze data, reflect on failures, and optimize models like scientists do [2][14][56]. Group 1: Framework and Mechanism - DrSR employs a dual-path reasoning mechanism that integrates "data insights" and "experience summaries" to guide large models in scientific equation discovery [16][28]. - The framework consists of three virtual scientists: a data scientist, a theoretical scientist, and an experimental scientist, each contributing to a collaborative mechanism for efficient scientific equation discovery [3][7]. Group 2: Performance and Results - In various interdisciplinary modeling tasks, DrSR has demonstrated superior generalization capabilities, outperforming existing methods in accuracy and efficiency [4][30]. - Experimental results show that DrSR achieved an accuracy of 99.94% in nonlinear damping oscillation system modeling, significantly surpassing all baseline methods [31]. Group 3: Learning and Adaptation - DrSR's process is a closed loop: data analysis → prompt guidance → equation generation → evaluation and scoring → experience summarization, allowing the model to accumulate knowledge and refine its approach [28]. - The framework's experience-driven strategy helps avoid common failure structures, resulting in a higher proportion of valid equations generated compared to other methods [37]. Group 4: Robustness and Generalization - DrSR exhibits strong robustness against noise and out-of-distribution (OOD) data, maintaining low normalized mean square error (NMSE) across various tasks [40][41]. - The model's performance remains stable under different Gaussian noise levels, showcasing its generalization advantages [41]. Group 5: Future Directions - DrSR is integrated into the ScienceOne platform, providing efficient and interpretable scientific modeling services, with plans to enhance its reasoning capabilities and cross-task generalization [57]. - Future improvements will focus on expanding DrSR's capabilities to multi-modal scientific modeling scenarios and incorporating continuous learning mechanisms [61].