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Kuaishou opensources the KAT-V1 autonomous thinking large model, which includes two versions: 40B and 200B. The 40B version performance is close to DeepSeek-R1, and the 200B version outperforms several flagship models. The model innovatively adopts a mixed training paradigm of short and long-term thinking and the Step-SRPO reinforcement learning algorithm, which can automatically adjust the thinking mode based on the complexity of the question, solving the problem of overthinking. Based on Qwen2.5-32B, it achieves excellent performance in fields such as science and code through an heterogeneous distillation framework and pre-training with 10 million examples.

Anthropic introduces AI audit agents (investigation, evaluation, red-teaming) to enhance model alignment testing. Agents enable parallel audits, detect biases with 42% success rate, addressing manual audit limitations. Code open-sourced on GitHub.....

Memories.ai secures $8M funding for AI that analyzes 10M hours of video, surpassing 1-2 hour limits. Targets security/marketing with noise reduction, compression, and indexing. Faces competition but leads in scalability and edge computing.....

Alibaba's Qwen3-Coder tops Hugging Face's model rankings as the hottest open-source AI coding model, outperforming GPT-4.1 and Claude4 with its MoE architecture, excelling in multi-agent tasks, and surpassing 20M downloads. Tech leaders praise it as a breakthrough for Chinese open-source AI.....

The Qwen-MT machine translation model is officially released. Qwen-MT is based on the powerful Qwen 3 model, and has been trained on a massive amount of multilingual and translation data. It combines reinforcement learning technology to significantly improve the accuracy and fluency of translation results. Developers can experience its fast and accurate translation capabilities directly through the Qwen API (qwen-mt-turbo). The core highlights of Qwen-MT include supporting bidirectional translation between 92 languages, covering more than 95% of the global population, and meeting a wide range of language communication needs.

The ByteDance Seed team officially announced its latest research achievement - the Seed LiveInterpret 2.0 end-to-end simultaneous translation large model. The release of this model marks a major breakthrough in machine simultaneous translation technology, with its translation accuracy approaching that of professional interpreters, and an extremely low latency of only 3 seconds. It also features real-time voice cloning, allowing the translated speech to be output in the speaker's tone, greatly enhancing the naturalness and fluency of cross-language communication.

Kuaishou and Shanghai Jiao Tong University announced the open-source multimodal generation model Orthus at the ICML conference. The model is based on an autoregressive Transformer architecture and demonstrates excellent performance in text-image conversion tasks, with significantly improved computational efficiency compared to existing models. Orthus uses a unique architectural design, separating processing of text and image features through specific modal generation heads, and efficiently fusing them in a unified representation space. Research shows that the model outperforms professional models in image understanding and text-to-image tasks, while also demonstrating strong potential in application scenarios such as image editing, making it a breakthrough in multimodal generation.

On July 23, Kunlun Group introduced two innovative AI models to global users - the Mureka V7 Music Model and the Mureka TTS V1 Audio Model. The launch of these two models marks an important step forward in the field of AI music generation. Since its release, the Mureka V7 Music Model has attracted widespread attention, gaining extensive praise for its efficient music creation capabilities and outstanding music quality. Users simply need to input lyrics, select a style or theme, and click generate to obtain a complete and pleasant song in a short period of time.

The夸克 Health Large Model has passed the written evaluation of 12 core medical disciplines by chief physicians in China, becoming the first large model to reach this level domestically. The model has been integrated into夸克AI Search, allowing users to access chief physician-level AI medical services through deep search. Its core breakthrough lies in establishing a slow-thinking capability, processing complex medical issues through chain reasoning and clinical deduction path modeling.夸克 adopts a dual data production line + dual reward mechanism training system, with a team of thousands of physicians labeling the data, including over 400 deputy chief physicians and above. The platform currently has more than 20 million monthly active medical student users.

The Meta research team introduced an innovative architecture called AU-Net, breaking through the limitations of traditional tokenization techniques. The model learns directly from bytes, dynamically combining bytes into hierarchical sequence representations through a self-regressive U-Net structure. Its contracting path compresses bytes into semantic units, while the expanding path restores information and fuses details, with skip connections preserving local features. A multi-stage pooling strategy is used to extract semantics at the word and phrase levels, and multi-linear upsampling optimizes information fusion. The self-regressive generation mechanism ensures text coherence and significantly improves inference efficiency. This architecture

The Kimi team released the Kimi K2 technical report. This trillion-parameter large model, with 32 billion activated parameters, won the global open-source model competition, with performance comparable to top closed-source models such as GPT4.5. Its innovations include: using the MuonClip optimizer to process 15.5 trillion token data without loss; developing a multi-domain tool data synthesis pipeline; pioneering the restatement method to improve training efficiency, achieving the effect of ten rounds of original data in one round of rewritten data; optimizing model performance through supervised fine-tuning and reinforcement learning. The model is based on N

Latent Labs launches a revolutionary AI model called LatentX, focusing on innovative protein design. The model, developed under the leadership of Simon Kohl, a former DeepMind core member, can generate new protein structures with atomic-level accuracy and high experimental feasibility. Unlike prediction tools like AlphaFold, LatentX has the ability to creatively design proteins. The company uses a licensing model, offering services to academic institutions and pharmaceutical companies, supported by $50 million in funding. The future plans include launching premium paid features.

Alibaba's Qwen3-Coder, a 48B-parameter MoE-based AI model, excels in complex programming tasks with 256K context support. Trained on 7.5TB data, it outperforms GPT4.1 in benchmarks and is open-sourced on ModelScope and Alibaba Cloud.....

On July 22, Tencent HuanYuan announced that its self-developed ASR (Speech Recognition) large model has been officially applied to the ima platform, achieving voice input functionality on mobile app interfaces for the first time, offering users a convenient experience of 'speaking fluently'. Users can now directly input questions or record inspirations through voice, without manually typing on the keyboard, greatly improving input efficiency. Tencent HuanYuan's ASR large model stands out with its keen auditory perception and intelligent understanding capabilities, accurately recognizing speech even in complex environments, capable of recognizing 300 words per minute, which is faster than

On July 22, 01.AI held a product launch event in Beijing, officially launching the upgraded WanZhi Enterprise Large Model Platform 2.0 version, and simultaneously introducing an enterprise-level Agent intelligent entity, marking a significant step forward for AI technology in the enterprise application field. The newly released WanZhi Platform 2.0 and enterprise-level Agent aim to provide customized, high-performance AI solutions for enterprises through deep thinking and task planning capabilities. At the event, Dr. Li Kaifu, CEO of 01.AI, pointed out that there is a significant gap between foundational models and industrial applications at present.

On July 23, Alibaba Cloud officially announced that its latest AI programming large model, Qwen3-Coder, is fully open-sourced, which quickly sparked a buzz in the field of intelligent programming. Qwen3-Coder has achieved top performance among open-source models in agentic coding, agentic browser use, and basic coding tasks, thanks to its outstanding code generation and agent capabilities, marking a new stage in intelligent programming technology. The Qwen3-Coder model series provides

Today, iFLYTEK officially launched the intelligent office device X5. This world-first office device equipped with a local large model is now available on the market starting at 4999 yuan. The X5 features a 10.65-inch E Ink e-ink screen with a 300 PPI display resolution. The device weighs only 355 grams and has a thickness of 4.6 millimeters, becoming the lightest and thinnest e-ink device of its size in the world. The product comes in standard and Pro versions: the standard version includes a light gray leather cover and a gray body (6G+64G, 4999 yuan), while the Pro version offers a ink blue leather cover and a golden body,