腾讯姚顺雨团队发布署名论文，让模型“上下文学习”真正走向现实

Core Insights - The article discusses the challenges faced by current language models in learning from context, highlighting that even the strongest models struggle with this capability [1][2][3] Group 1: Research Findings - Tencent's research team, in collaboration with Fudan University, emphasizes that enabling large models to learn from context is more difficult than previously thought [2][3] - The team developed CL-bench, a benchmark designed to evaluate whether language models can learn new knowledge from context and apply it correctly, consisting of 500 complex contexts, 1,899 tasks, and 31,607 validation standards [3] - The top ten language models achieved an average task resolution rate of only 17.2% on CL-bench, indicating significant shortcomings in their ability to utilize context [3] Group 2: Future Implications - The research suggests that enhancing models' context learning capabilities could shift the role of humans from being primary data providers to context providers, changing the competitive landscape in AI [3][4] - The team also notes that memory management in models may become a core theme in the development of large models by 2026, potentially leading to autonomous learning capabilities [4]