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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

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, the MiMo-VL multi-modal model developed by Xiaomi Company has taken over the baton from MiMo-7B, showcasing powerful capabilities in multiple fields. The model significantly outperforms the same-size benchmark multi-modal model Qwen2.5-VL-7B in tasks such as general question answering and understanding inference for images, videos, and language, and its performance in GUI Grounding tasks is comparable to that of specialized models, preparing for the arrival of the Agent era.

Recently, a new evaluation benchmark - RBench-V, specifically designed to test the visual reasoning capabilities of multi-modal large models, was released by research teams from Tsinghua University, Tencent HUNYUAN, Stanford University, and Carnegie Mellon University. The introduction of this benchmark aims to fill the gap in the current evaluation system regarding the model's visual output capabilities, allowing for a more comprehensive understanding of existing model performance. The RBench-V benchmark consists of 803 questions covering multiple fields, including geometry and graph theory, mechanics and electromagnetism, multi-target recognition, and path planning.

Researchers in China have unveiled the world's first three-dimensional atlas of a brain-computer interface at the micrometer scale. This groundbreaking achievement significantly improves the safety and precision of brain surgery involving BCIs.

Recently, a miniature deep-sea multi-modal robot jointly developed by Beihang University, the Institute of Deep-Sea Science and Engineering of the Chinese Academy of Sciences, and Zhejiang University, was successfully published in the top international academic journal Science Robotics. This robot has milestone significance in ocean exploration and scientific research, particularly in deep-sea applications. Measuring less than 50 centimeters long and weighing only 1500 grams, this remarkable robot can perform multi-modal movement at a depth of 10,000 meters. This means it can not only swim, but also glide and crawl, demonstrating exceptional capabilities.

On March 6th, the Beijing Academy of Artificial Intelligence (BAAI) announced the open-sourcing of its multimodal vector model, BGE-VL. This achievement marks a significant breakthrough in the field of multimodal retrieval. The BGE-VL model achieves state-of-the-art results on various multimodal retrieval tasks, including image-text retrieval and compositional image retrieval, significantly improving the performance of multimodal retrieval.

Samsung recently announced that the Galaxy S25 series launched in the Chinese market will now integrate the DeepSeek-R1 large language model, significantly enhancing its AI functionality. According to Samsung China, users can access features including in-depth reasoning, internet search, emotional intelligence, contextual memory, and visual analysis without downloading additional apps or registering accounts. Simply update the Samsung Members app to the latest version or access DeepSeek-R1 through Samsung's Smart Home or Bixby's SmartThings.

DeepSeek launched an official account on Zhihu and published a technical article, "DeepSeek-V3/R1 Inference System Overview," detailing its model inference system optimization and cost-profit margin information for the first time, marking the end of the highly anticipated DeepSeek Open Source Week. The article highlights two key optimization goals for the DeepSeek-V3/R1 inference system: higher throughput and lower latency. To achieve these goals, DeepSeek employs large-scale cross-node expert parallelism (EP) technology, although...