Recently, the DeepSeek team in China has launched its latest open-source large model R1, which has gained widespread attention. The performance of the R1 model is exceptionally outstanding, surpassing OpenAI's o1 model in multiple tests, especially excelling in assessments related to mathematics and programming. In the latest AIME2024 test in the United States, R1 scored 79.8, outperforming o1's score of 79.2. In the MATH-500 test, R1 achieved a score of 97.3, again leading over o1.

On January 15, 2025, MiniMax announced the open sourcing of its new series of models, MiniMax-01, which includes the foundational language model MiniMax-Text-01 and the visual multimodal model MiniMax-VL-01. The MiniMax-01 series features bold innovations in its architecture, implementing linear attention mechanisms on a large scale for the first time, breaking the limitations of traditional Transformer architectures. With a parameter count reaching 456 billion and 45.9 billion activations per instance, its overall performance competes with international standards.

Recently, research teams from Tsinghua University, Fudan University, and Stanford University jointly released an Agent development framework called 'Eko'. This framework aims to help developers quickly build production-ready 'virtual employees' using simple code and natural language. The Eko framework can take over a user's computer and browser, performing various tedious tasks on behalf of humans. With Eko, users can achieve automated data collection, testing, and file management, among other functionalities. For instance, users can set Eko to automatically gather information from Yahoo Finance.

ViTPose is an open-source action estimation model that excels at recognizing human postures, as if it can understand the actions you are performing. The standout feature of this model is its simplicity and efficiency; it does not use complex network structures but directly employs a technique called Vision Transformer. The core of ViTPose uses a pure Vision Transformer, which acts like a powerful 'skeleton' to extract key features from images. Unlike other models, it does not require complexity.

Stanford University has made significant advancements in the field of artificial intelligence with their latest development, the open-source STORM & Co-STORM system. This system can generate high-quality long articles by integrating diverse information through simple topic input. This innovation not only avoids information blind spots but also greatly improves the efficiency and quality of scientific writing. The core technologies of the STORM & Co-STORM system include support from Bing Search and GPT-4o mini.

American technology company Nvidia has recently completed its acquisition of Israeli software company Run:ai, with the collaboration aimed at further enhancing the management efficiency of AI cloud computing. Although the specific acquisition amount has not been disclosed, reports suggest the deal is valued at approximately $700 million. Nvidia announced that it will open source Run:ai's software to support the AI ecosystem more broadly. Run:ai's core software helps clients efficiently schedule Nvidia GPU resources, thereby optimizing AI computations.

ZhiYuan Robotics, in collaboration with the Shanghai Artificial Intelligence Laboratory and other institutions, has launched the AgiBot World dataset, the world's first million-level robot dataset based on real-world scenarios. Compared to Google's Open X-Embodiment, the scale of long-range data has increased tenfold, the scene coverage has expanded one hundredfold, and the data quality meets industrial standards. This open-source project, hailed as the 'ImageNet moment' in the field of embodied intelligence, showcases remarkable robotic capabilities, from arranging flowers in the living room to cooking in the kitchen and handling checkout in supermarkets.

The Beijing Academy of Artificial Intelligence (BAAI) recently announced that it has collaborated with ecological partners to build and open source the Heterogeneous Unified Communication Library FlagCX, aiming to address the challenges faced by communication libraries in the era of diverse computing power and fill the significant gap in the open source software stack for diverse computing power. This initiative responds to the special action organized by relevant national departments for the governance of typical issues in 'Clear and Bright: Algorithmic Problems of Online Platforms', reflecting the important principles of correct algorithm orientation, fairness and justice, and transparency.

The GLM-PC base model CogAgent-9B under Zhipu AI has now been open-sourced to promote the development of the large model Agent ecosystem. CogAgent-9B is a specialized Agent task model trained based on GLM-4V-9B, capable of predicting the next GUI action based solely on a screenshot input, combining user-specified tasks with historical operations. This model's versatility makes it widely applicable to various GUI interaction scenarios such as personal computers, smartphones, and vehicle systems.

The highly anticipated Deepseek V3 is finally open source! This brand-new AI model has achieved significant breakthroughs in multilingual programming capabilities, even outperforming competitors like Claude 3.5 Sonnet V2 in the aider multilingual programming assessment, attracting widespread attention in the industry. It is reported that Deepseek V3 has made a qualitative leap in performance compared to previous versions. Deepseek V2.5 had a success rate of only 17% in the aider assessment, while V3 has surged to 4.