Core Insights - The article discusses the SE-Agent framework, which significantly enhances the problem-solving capabilities of LLM-based agents by introducing a self-evolution mechanism that improves solution diversity and collaboration among different trajectories [2][3][22]. Group 1: SE-Agent Framework Overview - SE-Agent represents a shift from independent attempts to collective evolution, allowing agents to learn from their entire problem-solving paths rather than treating each attempt as an isolated event [6][15]. - The framework has achieved a Top-1 Resolution Rate of 80% on the SWE-Bench Verified benchmark, showcasing its effectiveness in complex reasoning tasks [2][11]. Group 2: Evolutionary Operators of SE-Agent - The three main evolutionary operators of SE-Agent are: 1. **Revision**: This involves generating diverse initial trajectories and refining them through self-reflection and targeted improvements [8]. 2. **Recombination**: This operator promotes knowledge sharing between trajectories, allowing for the combination of effective segments from different paths to create stronger solutions [9]. 3. **Refinement**: A multi-dimensional evaluation function assesses all trajectories, ensuring the retention of high-scoring paths while maintaining diversity [10]. Group 3: Performance Metrics - SE-Agent has shown significant performance improvements across various models, with Claude-3.7-Sonnet achieving a 61.2% success rate on the first attempt, marking a record for open-source frameworks [14][18]. - Other models also demonstrated substantial relative improvements, such as DeepSeek-V3 increasing from 31.6% to 54.8% [12]. Group 4: Case Study and Practical Implications - A case study involving a real bug fix in scikit-learn illustrated how SE-Agent effectively avoided "tunnel vision" by exploring different directions, ultimately leading to a successful resolution [20][21]. - This case exemplifies the framework's ability to discover deeper, more critical solutions through evolutionary processes at the trajectory level [21]. Group 5: Future Directions - The SE-Agent framework lays the groundwork for developing self-evolving intelligent systems, with plans to extend its principles to broader path search problems, including reinforcement learning and embodied intelligence planning [24].