A Technical Tour of the DeepSeek Models from V3 to V3.2

DeepSeek has introduced new AI models that are comparable to existing offerings in the market, raising questions about the company's business strategy and approach to open-source technology. This development comes as the company aims to position itself against major competitors like Google and OpenAI.

A new Mixture-of-Ranks (MoR) architecture has been proposed for one-step real-world image super-resolution (Real-ISR), integrating sparse Mixture-of-Experts (MoE) to enhance the adaptability of models in reconstructing high-resolution images from degraded samples. This approach utilizes a fine-grained expert partitioning strategy, treating each rank in Low-Rank Adaptation (LoRA) as an independent expert, thereby improving the model's ability to capture heterogeneous characteristics of real-world images.

DeepSeek has launched new AI models aimed at competing with industry giants Google and OpenAI, marking a significant step in the global AI race. This development highlights the ongoing efforts to create advanced AI systems capable of complex reasoning and problem-solving.

Mistral AI has launched the Mistral 3 family, a suite of 10 open-source AI models designed for various platforms, including laptops, drones, and edge devices. This release marks a significant step in the company's strategy to compete against major players like OpenAI and Google by providing accessible AI solutions across different applications.

DeepSeek is set to launch its new AI model, R2, after delays attributed to limited access to computing resources. This development is expected to heighten the ongoing competition between the U.S. and China in the artificial intelligence sector.

DeepSeek has made headlines again with its recent advancements in artificial intelligence, particularly with the introduction of its new model, DeepSeekMath-V2, which has achieved gold medal status at both the International Mathematical Olympiad 2025 and the Chinese Mathematical Olympiad 2024. This achievement highlights the company's growing influence in the AI sector.

Recent advancements in Diffusion Mixture-of-Experts (MoE) models have shifted focus from routing mechanisms to architectural configurations, revealing that factors like expert modules and attention encodings are crucial for model effectiveness. This systematic study emphasizes the importance of tuning these configurations to maximize performance in both latent and pixel-space diffusion frameworks.