张钹、杨强与唐杰、杨植麟、林俊旸、姚顺雨（最新3万字发言实录）

Core Insights - The AGI-Next conference highlighted the current challenges and future opportunities in AI development, particularly focusing on the capabilities and limitations of large models [3][4][5]. Group 1: Key Discussions on AGI and AI Development - Zhang Bo emphasized five fundamental deficiencies in current large models, advocating for a definition of AGI that includes executable and verifiable capabilities [3]. - Yang Qiang discussed the differentiation of agents based on their ability to autonomously set and plan goals, rather than relying on human-defined parameters [3]. - Tang Jie noted that while scaling remains a valid approach, the true exploration should focus on enabling models to possess autonomous scaling capabilities [4]. Group 2: Scaling and Model Capabilities - Yang Zhilin explained that the essence of Scaling Law is to convert energy into intelligence, emphasizing the importance of efficient approaches to reach the limits of intelligence [4]. - Lin Junyang expressed optimism about the potential for Chinese teams to achieve global leadership in AI within the next 3-5 years, estimating a 20% probability of success [4]. - Yao Shunyu highlighted the differentiation between vertical integration and layered model applications, suggesting that model companies may not necessarily excel in application development [4]. Group 3: Future Directions and Challenges - The discussion pointed out that the path from scaling to genuine generalization capabilities remains a core challenge for AI models [12][14]. - The need for models to develop memory and continuous learning structures akin to human cognition was identified as a critical area for future research [35][36]. - The exploration of self-reflection and self-awareness capabilities in AI models was deemed a significant yet controversial topic within the academic community [36][47]. Group 4: Technical Innovations and Model Architecture - The introduction of new optimization techniques, such as the Muon optimizer, was highlighted as a means to enhance token efficiency and overall model performance [55][58]. - The development of the Kimi Linear architecture aims to improve linear attention mechanisms, making them more effective for long-context tasks [64]. - The integration of diverse data sources and the enhancement of model architectures are seen as essential for achieving better agent capabilities in AI [67].