Skills刚火，就有零Skill的Agent来了…

Core Viewpoint - The article discusses a new paradigm in AI agents that can autonomously create tools to fulfill tasks without human intervention, showcasing significant advancements in self-evolving capabilities [1][2][3]. Group 1: Agent Capabilities - The agent can independently evolve and create tools based on task requirements, demonstrating a level of autonomy previously unseen in AI [3][19]. - In a benchmark test known as Humanity's Last Exam (HLE), the agent outperformed others, achieving a score nearly 20 points higher than undisclosed methods that utilized tools [4][5]. - The agent successfully created 128 tools during its evaluation, indicating a robust ability to adapt and generate resources as needed [19][20]. Group 2: Performance Metrics - The agent's performance showed a rapid initial increase in tool creation, stabilizing at 128 tools, which were deemed sufficient for most tasks [28][33]. - A comparative analysis of different strategies revealed that the agent's performance improved significantly with the reuse of existing tools, leading to fewer new tools being created as the task complexity increased [34][35]. Group 3: Self-Evolution Framework - The concept of in-situ self-evolution allows the agent to learn and adapt during the inference phase without external supervision, relying on internal feedback and past experiences [52][53]. - This framework emphasizes the importance of tools as the primary means of evolution, allowing the agent to expand its capabilities dynamically [62][63]. - The agent's architecture includes roles such as Manager, Tool Developer, Executor, and Integrator, facilitating a structured approach to task completion and tool creation [68][71]. Group 4: Industry Implications - The research highlights a shift towards open-source solutions in AI, with the potential for widespread application in various industries, particularly in scenarios requiring adaptability and low operational costs [88][126]. - The findings suggest that the agent's ability to self-evolve could address challenges in traditional AI models, such as high costs and limited flexibility in handling diverse user needs [106][114].