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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

Recently, at Baidu's AI Open Day, Wenxin Kaima, Baidu's intelligent code assistant, achieved a major breakthrough. Its standalone AI-native development environment tool, Comate AI IDE, was officially launched. This industry's first multimodal, multi-agent collaborative AI IDE not only introduces the groundbreaking feature of converting design drafts into code with one click, but also provides efficient, intelligent, and secure programming experiences for domestic enterprises and developers.

This article provides a detailed review of the AI-driven social media management tool SchedPilot, analyzing its core functions such as cross-platform publishing and AI comment assistant, helping creators and marketing teams determine whether this tool is worth using. Understand the differentiated advantages and applicable scenarios of SchedPilot compared to other social media management tools.

In recent years, the field of artificial intelligence has undergone tremendous changes, especially with large language models (LLMs) making remarkable progress in multi-modal tasks. These models have demonstrated strong potential in their ability to understand and generate language, but most current multi-modal models still adopt auto-regressive (AR) architectures, which limit inference processes to be rather monotonous and lacking flexibility. To address this limitation, a research team from The University of Hong Kong and Huawei Noah's Ark Lab has proposed a brand new model – FUDOKI, aiming to break these constraints. The core innovation of FUDOKI is

Tomorrow, the 2025FORCE Original Power Conference will be held in grand style. During the conference, the capability upgrade of the BeanPod Large Model family will be revealed. At the same time, the highly anticipated new BeanPod Video Generation Model will also be officially released. It is reported that this new model has multiple excellent features. In terms of multi-camera narration, through efficient model structure design, as well as multimodal positional encoding and multitask unified modeling technology, the model can seamlessly support unique and stable multi-camera expression, making video narration more smooth and natural.

The AI-driven video editing tool Diffusion Studio Pro has officially launched, drawing significant attention from the industry. This innovative product, which claims to combine 'CapCut + Cursor', offers a non-linear editing experience based on a browser with a local-first approach. It integrates over 16 generative AI models, providing strong support for professional video creators and developers. Key highlights: Multi-modal AI Empowers Non-linear Editing Diffusion Studio Pro is a tool completely based on

Recently, tech giant NVIDIA, in collaboration with the Massachusetts Institute of Technology (MIT) and the University of Hong Kong, released a new framework called Fast-dLLM. This innovative framework aims to significantly increase the inference speed of diffusion models (Diffusion-based LLMs), up to 27.6 times, providing stronger technical support for AI applications. The challenges and opportunities of diffusion models make them strong competitors to traditional autoregressive models.

Recently, the Natural Language Intelligence Team of the Tongyi Lab officially released and open-sourced VRAG-RL — a multi-modal RAG reasoning framework driven by visual perception. It aims to solve the problem of how AI can retrieve key information and conduct fine-grained reasoning from visual languages such as images, tables, design drafts, etc., in real-world business scenarios. Retrieving and reasoning about key information in complex visual document knowledge bases has always been a major challenge in the AI field. Traditional retrieval-augmented generation (RAG) methods struggle when handling visually rich information because they find it difficult to...