Recently, researchers from Stanford University and the University of Hong Kong discovered that current AI Agents (such as Claude) are more easily disrupted by pop-ups than humans, with significantly reduced performance even when faced with simple pop-ups. According to the study, AI Agents achieved an average attack success rate of 86% when confronted with deliberately designed pop-ups in experimental environments, leading to a 47% decrease in task success rate. This finding has sparked new concerns regarding the safety of AI Agents, especially as they are assigned more autonomous tasks.

ControlMM is an innovative technical framework developed jointly by the Chinese University of Hong Kong and Tencent, aimed at addressing challenges in multi-modal full-body motion generation. This framework supports multi-modal inputs from text, speech, and music to generate full-body motions that match the content. It employs the ControlMM-Attn module to process dynamic and static human topology in parallel, achieving efficient movement knowledge learning. A phased training strategy is adopted, progressing from text to motion pre-training, then to multi-modal control adaptation, ensuring the model's effectiveness under various conditions.

A research team from The Chinese University of Hong Kong and the Chinese Academy of Sciences has proposed a full modal pre-training paradigm called MiCo (Multimodal Context). This method has achieved significant results in the field of multimodal learning, setting new state-of-the-art (SOTA) records in 37 categories.