LLM+Tool Use 还能撑多久？下一代 AI Agent 在 self-evolving 的技术探索上行至何方？

Group 1 - The article discusses the increasing demand for self-evolving capabilities in AI agents, highlighting the limitations of static models in adapting to new tasks and dynamic environments [6][8][10] - It emphasizes the need for a systematic theoretical framework to guide the exploration of self-evolving agents, with contributions from multiple research institutions [8][10] - The article outlines three key dimensions for analyzing and designing self-evolving agents: what to evolve, when to evolve, and how to evolve, each addressing different aspects of the evolution process [9][10][11] Group 2 - The article raises questions about the ability of AI application companies to replicate or surpass the commercial successes of the mobile internet era, focusing on new monetization models [2][3] - It explores the differences in user ecosystems and commercial boundaries between AI and the mobile internet era, questioning the necessity of multiple apps as AI becomes a platform capability [2][3] - The article discusses the varying attitudes of Chinese and American internet giants towards AI investments and how this may impact future competitiveness [2][3] Group 3 - The article presents insights from Dario Amodei on the profitability of large models despite significant accounting losses, suggesting that each generation of large models can be viewed as independent startups [3] - It discusses the natural drive for funding, computing power, and data investment that comes with advancements in large model capabilities [3] - The article highlights the implications of Scaling Law for AI enterprise growth and the potential consequences if it were to fail [3]