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Tencent recently released the official version of its HunYuan large language model series – HunYuan-T1. This new model, built upon the medium-scale HunYuan base model and extensively fine-tuned, demonstrates significantly improved reasoning capabilities, particularly excelling in deep thinking and complex problem-solving. Since the launch of the HunYuan T1-Preview in February, users have experienced faster and more insightful processing. The official release marks a significant upgrade to the product line. The HunYuan-T1 development team leveraged the latest Turbo...

In the field of deep learning, normalization layers are considered an indispensable component of modern neural networks. Recently, research led by Meta FAIR research scientist Zhuang Liu, titled "Transformer without Normalization Layers", has garnered significant attention. This research not only introduces a new technique called Dynamic Tanh (DyT), but also demonstrates the effectiveness of Transformer architectures even without traditional normalization layers.

On March 13th, Sesame unveiled its latest speech synthesis model, CSM, attracting significant industry attention. According to the official introduction, CSM adopts an end-to-end Transformer-based multimodal learning architecture. It understands contextual information to generate natural and emotionally rich speech with stunningly realistic sound. The model supports real-time speech generation, processing both text and audio inputs. Users can also control features such as tone, intonation, rhythm, and emotion by adjusting parameters, showcasing high flexibility. CSM is considered a breakthrough in AI speech technology.

Frustrated by the slow processing speed of large language models on long documents? Researchers from Tsinghua University have unveiled a groundbreaking technology – the APB parallel inference framework – that dramatically accelerates processing. Benchmark tests show this technology achieves a 10x speed improvement over Flash Attention when handling ultra-long texts. With the rise of models like ChatGPT, AI's ability to process vast amounts of text (hundreds of thousands of words) has increased significantly. However, this often comes at the cost of processing speed...

Recently, the research team at Meta Reality Labs has jointly released an innovative generative model called 'Pippo', which can generate a dense turnaround video with a resolution of up to 1K from a randomly taken photo. This breakthrough technology marks another significant advancement in the fields of computer vision and image generation. The core of the Pippo model lies in the design of its multi-view diffusion transformer. Unlike traditional generative models, Pippo does not require any additional inputs, such as fitted parameters.

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.

Against the backdrop of the global generative AI wave in 2022, YuanShi Intelligence (RWKV) completed tens of millions of RMB in angel round financing in December 2023, with investment from Tianji Capital. Following this financing, the company’s valuation doubled, and the funds will be used for team expansion, new architecture development, and product commercialization. The emergence of RWKV presents a strong challenge to the traditional Transformer architecture. With the advancement of large language models (LLMs), although the parameter scale of models is becoming increasingly large, issues like hallucination and accuracy persist.

An innovative point cloud compression technology (TSC-PCAC) developed by a joint team from the Chinese Academy of Sciences, Tongji University, and Ningbo University has achieved a significant breakthrough. This technology not only greatly improves the compression efficiency of point cloud data but also significantly reduces processing time, clearing technical obstacles for the development of 3D applications like AR/VR. In the context of the rapid advancement of 3D vision technology, point clouds, as a key data format for virtual and augmented reality, face tremendous transmission and storage challenges. A high-quality point cloud can contain millions of data points, with each point...

In today's digital era, image generation technology is developing at an astonishing pace. Recently, a research team from the National University of Singapore proposed a brand-new framework — OminiControl, aimed at enhancing the flexibility and efficiency of image generation. This framework combines image conditions and fully utilizes the pre-trained Diffusion Transformer (DiT) model, providing unprecedented control capabilities. In simple terms, as long as you provide the source image, OminiControl can utilize it.