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In the media Q&A session after today's Honor Magic V5 and AI Terminal Ecosystem Launch, Honor CEO Li Jian, CFO Peng Qiuen, and Product Line President Fang Fei had in-depth discussions with the media. During the event, Honor officially announced support for the MCP and A2A protocols, and revealed that it will collaborate deeply with partners such as Alibaba, BYD, and Midea in the fields of intelligent service ecosystem, smart vehicle networking, and smart home. Honor CEO Li Jian emphasized in the conversation that 'openness' is the core philosophy of Honor. He pointed out...

Zhipu AI officially open-sources its latest general vision model, GLM-4.1V-Thinking, based on the GLM-4V architecture, which introduces a chain-of-thought reasoning mechanism, significantly enhancing its capabilities for complex cognitive tasks. The model supports multimodal inputs such as images, videos, and documents, and excels in diverse scenarios including long video understanding, image question answering, subject problem-solving, text recognition, document interpretation, grounding, GUI Agent, and code generation, covering a wide range of industry application needs. GLM-4.1V-9B-Thinking

At the recent Zhipu Open Platform Industrial Ecosystem Conference held in Shanghai, a major development emerged in the field of artificial intelligence: Pudong Venture Capital Group and Zhangjiang Group jointly announced a strategic investment of up to 1 billion yuan in Zhipu, with the first installment already completed. This significant investment will provide strong support for Zhipu in building a trusted artificial intelligence infrastructure and accelerate its layout in the field of General Artificial Intelligence (AGI). In his keynote speech at the conference, Zhipu CEO Zhang Peng elaborated on two latest achievements in the company's efforts to move toward AGI in collaboration with ecosystem partners.

The TEN Agent team recently announced that its core models **TEN Voice Activity Detection (VAD)** and **TEN Turn Detection** are now open source, providing powerful technical support for building real-time, multimodal speech AI agents. This move marks a significant advancement in the TEN framework's efforts to promote the democratization and open-source collaboration of speech interaction technology. The following is the latest information compiled by AIbase, offering an in-depth analysis of these two core models.

Welcome to the AIbase [AI Daily Report] section! Spend three minutes a day to learn about the latest AI events, helping you understand AI industry trends and innovative AI product applications. For more AI news, visit: https://www.aibase.com/zh1. Baidu officially releases the WENXIN Large Model 4.5 series and fully opens it to the public, featuring ten new models with various parameter configurations. These models are trained and inferred using the PaddlePaddle framework, achieving a FLOPs utilization rate of 47%, and perform well in multi-modal text tasks.

Recently, Baidu officially announced the open-source release of its WENXIN Large Model 4.5 series, launching a total of ten models, including mixed expert (MoE) models with 47B and 3B activated parameters, as well as dense models with 0.3B parameters. This open-source initiative not only fully publicizes the pre-trained weights but also provides inference code, marking a significant advancement for Baidu in the field of large models. These newly released models can be downloaded and deployed on platforms such as PaddlePaddle Starry Sky Community and Hugging Face. Additionally, Baidu Intelligent Cloud's Qianfan Large Model Platform also provides

Baidu officially released the ERNIE Bot 4.5 series models and made them fully open source. Users can experience this latest open-source technology immediately through ERNIE Bot (https://yiyan.baidu.com). This series includes multiple parameter configurations, such as Mixture of Experts (MoE) models with activated parameters of 47B and 3B, as well as dense models designed with 0.3B parameters, totaling ten different models. In terms of training and inference, the ERNIE 4.5 series models use PaddlePaddle deep learning.

Baidu's WENXIN series large models have recently been open-sourced on its PaddlePaddle platform, including dozens of latest models such as ERNIE-4.5-VL-424B-A47B-Paddle and ERNIE-4.5-300B-A47B-Paddle. Although Baidu has not actively disclosed this open-source initiative, updates on the PaddlePaddle platform show that these actions were concentrated between June 29th and June 30th, marking its latest move. A source within the company confirmed: official

On June 29, 2025, the Alibaba International AI Team officially released the new multi-modal large model **Ovis-U1**, marking another major breakthrough in the field of multi-modal artificial intelligence. As the latest masterpiece of the Ovis series, Ovis-U1 integrates multi-modal understanding, image generation, and image editing functions, demonstrating powerful cross-modal processing capabilities, providing new possibilities for developers, researchers, and industry applications. This is a detailed report on Ovis-U1 by AIbase. Ovis-U1

On June 30, Huawei officially announced the open sourcing of the Pangu dense model with 7 billion parameters, the PanguPro MoE model with 72 billion parameters, and the model inference technology based on Ascend. This open-source initiative is a key step in Huawei's strategy to build an Ascend ecosystem, aiming to promote research and innovation in large model technology, accelerate the application of artificial intelligence across industries, and create value.

China's first open-source large model in the marine field, OceanGPT (Cangyuan), was officially launched in Hangzhou, Zhejiang. This innovative achievement was developed by the National Key Laboratory of Marine Precision Sensing Technology at Zhejiang University, marking a significant step forward for China in the field of marine technology. OceanGPT has basic capabilities for answering questions about marine knowledge and can interpret multi-modal data such as sonar images and marine observation maps in natural language. This capability allows OceanGPT to perform exceptionally well when handling complex marine data. The model also adopts