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Apple's MLX framework now supports CUDA, marking a strategic shift to embrace NVIDIA's ecosystem. This enables developers to train models on NVIDIA GPUs and deploy on Apple devices, enhancing efficiency and bridging platform gaps.....

Open-source RL framework ART released, enhancing AI Agent training with GRPO tech. Features Python support, small model compatibility, and client-server architecture for multi-step tasks like email automation and game AI.....

Recently, Kunlun Tech Skywork and Nanyang Technological University jointly launched an innovative hierarchical multi-agent collaboration framework called AgentOrchestra, which is referred to as the 'AI Symphony Orchestra,' marking a significant step forward in agent collaboration within the field of general artificial intelligence. Faced with complex tasks in the real world that challenge single models or agents, AgentOrchestra enables intelligent agents with distinct expertise to collaborate and tackle challenges together, by mimicking the collaborative model of a symphony orchestra. Traditionally, general AI

Apple's MLX framework now supports CUDA, allowing developers to create applications on Apple Silicon devices and export them to run in a CUDA environment. This move significantly reduces development costs, as small teams no longer need to purchase expensive Nvidia hardware upfront. The project, led by GitHub developers, has been integrated into the main MLX branch. Although it does not support running directly on Macs with Nvidia GPUs, development and testing can be fully completed on Apple devices. Performance in CUDA environments is significantly better than on Macs, and combining it with Nvidia hardware for AI applications is highly beneficial.

Hong Kong Polytechnic University and OPPO Research Institute jointly released the open-source video super-resolution framework DLoRAL, which generates high-definition videos in one step using diffusion models. The framework adopts a dual LoRA architecture: C-LoRA maintains temporal consistency between frames, while D-LoRA enhances spatial details. Through a two-stage training strategy, it optimizes temporal coherence and high-frequency information. Compared to traditional methods, DLoRAL improves inference speed by 10 times while maintaining smoothness, significantly enhancing image details, and providing an efficient open-source solution for video HD enhancement.

On June 18th, the artificial intelligence research institute OpenAI officially open-sourced its customer service agent framework, marking an important step in the company's layout of enterprise-level AI solutions. This framework aims to promote the practical commercial application of intelligent agent systems from laboratory environments to real-world scenarios by providing transparent toolchains and clear implementation examples.

At the 2025 WWDC conference, Apple announced the opening of its artificial intelligence technology to the developer community, releasing the Foundation Models framework and upgraded Xcode26, marking a major breakthrough for Apple in the field of AI development tools. Foundation Models Framework: Privacy-Focused AI Integration Solution The new Apple Foundation Models framework provides developers with a centralized set of tools to directly embed Apple's self-developed AI models into applications.

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...

Recently, Google officially released the open source framework LMEval, aimed at providing standardized evaluation tools for large language models (LLMs) and multimodal models. The launch of this framework not only simplifies cross-platform model performance comparisons, but also supports assessments in areas such as text, images, and code, showcasing Google's latest breakthroughs in the field of AI evaluations. AIbase has compiled the latest developments of LMEval and its impact on the AI industry. Standardized Evaluations: Simplified Cross-Platform Model Comparisons