Search AI Products and News

On June 26, 2025, ByteDance officially launched its latest image synthesis technology - XVerse, aimed at providing a high-precision multi-subject image generation solution. This innovative technology enables users to independently and precisely control multiple individuals, greatly enhancing the ability to generate personalized and complex scenes. The core of XVerse lies in its unique DiT modulation method, which allows control over the identity and semantic attributes of each subject without affecting the overall latent features of the image. By converting reference images into specific characteristics...

Earth System Models are crucial tools for predicting environmental changes, helping us better prepare for the future. However, these models have extremely high computational demands, limiting their operation at fine enough resolutions. Currently, most models operate at a resolution of approximately 100 kilometers, equivalent to the size of Hawaii, making accurate predictions for specific regions difficult. Nevertheless, for practical applications such as agriculture, water resource planning, and disaster response, city-scale prediction accuracy around 10 kilometers is vital. Therefore, improving the resolution of these models is essential for better protection.

Recently, the Windows version of Sogou Input Method has undergone a major upgrade with the launch of the new AI Wanzai Quick Search function, providing users with a more efficient and convenient office and study experience. The AI Wanzai Quick Search function is characterized by its one-click trigger and fast response. Users no longer need to follow the traditional three-step search process of opening a browser👉🏻search engine👉🏻input content. They can directly type on any spot on the desktop or use the shortcut key Ctrl+Shift+Space”, which can summon the All-Purpose Quick Search” function within 0.5 seconds, allowing for faster information retrieval.

Researchers from Hong Kong and the UK have recently proposed a novel image tokenization method designed to convert images into digital representations (tokens) in a more compact and accurate manner. Unlike traditional methods that distribute information uniformly across all tokens, this method employs a hierarchical structure, capturing visual information layer by layer, thereby improving the quality and efficiency of image reconstruction. Traditional image tokenization techniques typically divide each part of an image equally into multiple tokens, whereas the new method adopts a hierarchical structure. Initial tokens encode rough shapes and structural elements,

Researchers from DeepSeek and Tsinghua University recently published a new paper exploring scaling methods for reward model inference, seemingly advancing DeepSeek R2. Reinforcement learning is widely used in the large-scale post-training phase of large language models, but faces the challenge of obtaining accurate reward signals for these models. The researchers found that using pointwise generative reward modeling (GRM) improves model adaptability and scalability during inference. To this end, they propose Self-Principle Calibration Tuning (SPCT) learning.

Tencent Sogou Input Method announced that its PC version "AI Assistant" has officially integrated the full-fledged "Tencent Yuanbao" large language model, bringing users a more convenient AI experience. This upgrade requires no additional AI software download; users can directly experience the powerful functions of Tencent HunYuan and DeepSeek large language models on their keyboards, supporting "deep thinking" and "online search", helping to significantly improve office and learning efficiency.

Recently, the successful launch of the VLM-R1 project has brought new hope to this field. This project represents the successful migration of the R1 Method from the DeepSeek team into visual language models, indicating that AI's understanding of visual content will enter a whole new phase. The inspiration for VLM-R1 comes from last year's open-source R1 Method by DeepSeek, which leverages GRPO (Generative Reward Processing Optimization) reinforcement.

Recently, researchers from the Department of Engineering at Princeton University and the Indian Institute of Technology achieved breakthroughs in the design of wireless microchips using artificial intelligence (AI), significantly reducing design time and costs while discovering new functions. The results of this research were published in the journal Nature Communications, marking the dawn of a new era in microchip design. Image source note: The image was generated by AI, image licensed by Midjourney. Microchips play a vital role in modern technologies such as wireless communication. They consist of traditional electronic circuits and complex electromagnetic structures.

In the age of AI, large language models (LLMs) are akin to martial arts secrets; their training processes consume massive computing power and data, much like martial arts experts who have spent years in secluded training. The release of open-source models is like these experts sharing their secrets with the public, but they come with certain licenses (such as Apache 2.0 and LLaMA 2 Community License) to protect their intellectual property (IP). However, the world can be treacherous, and incidents of 'shelling' are bound to occur. Some developers claim they have trained new LLMs when, in fact, they are using other foundational models (like Lla

The Sogou Input Method version 12.0 has recently undergone a smart upgrade, introducing a series of new features. The key highlights of this update include AI search, AI quick check, AI companion, magic gestures, and a brand-new visual design. In terms of AI search, version 12.0 of the Sogou Input Method, in collaboration with Tencent's Hunyuan Large Model, enables a feature that allows users to search while typing. Users can quickly obtain search results by simply clicking the magnifying glass icon, significantly improving search efficiency. Additionally, PC users can swiftly gather information through the 'equal sign for word selection' feature, enhancing productivity.

Recently, a new study from the University of Michigan found that an energy-efficient method for training large language models can achieve similar results in the same amount of time while reducing energy consumption by 30%. This method can save enough energy to power 1.1 million American households by 2026. The researchers developed a software tool named Perseus that identifies the critical path, which is a series of subtasks requiring the longest time to complete. Then, Perseus reduces the processor speed on non-critical paths so that all tasks can finish simultaneously.