万亿思考模型新速度！蚂蚁开源Ring-2.5-1T：IMO金牌水平，强；混合线性架构，快！

Core Viewpoint - Ant Group has launched the world's first open-source hybrid linear architecture trillion-parameter model, Ring-2.5-1T, which excels in mathematical logic reasoning and long-range autonomous execution capabilities [2][3]. Group 1: Model Capabilities - Ring-2.5-1T achieved a gold medal level score of 35 in IMO and an impressive score of 105 in CMO, significantly surpassing national training team standards [3]. - The model can independently handle complex tasks such as search and coding, demonstrating its robust task execution abilities [3][8]. - It has broken the industry norm that deep reasoning requires sacrificing inference speed and memory usage, achieving a 3x increase in throughput while reducing memory usage to below 1/10 during long sequence generation [5][7][16]. Group 2: Architectural Innovations - The model employs a hybrid linear attention architecture, evolving from the Ring-flash-linear-2.0 technology, utilizing a 1:7 design of Multi-Head Latent Attention (MLA) combined with Lightning Linear Attention [9]. - Incremental training methods were used to maintain strong reasoning capabilities while achieving linear inference speeds, converting parts of the original GQA layers to Lightning Linear Attention [12]. - The activation parameter count increased from 51 billion to 63 billion, yet inference efficiency saw significant improvements compared to Ling 2.0 [15]. Group 3: Training Mechanisms - A dense reward mechanism was introduced to enhance logical reasoning, focusing on the rigor of the reasoning process, which significantly reduced logical flaws and improved advanced proof techniques [18]. - The model underwent large-scale asynchronous Agentic Reinforcement Learning training, enhancing its autonomous execution capabilities in long-chain tasks [18]. Group 4: Practical Applications - In practical tests, Ring-2.5-1T successfully solved complex abstract algebra proof problems, demonstrating high logical sensitivity and rigorous reasoning [20][24]. - The model also showcased its programming skills by writing a high-concurrency thread pool in Rust, effectively managing memory safety and concurrency [27]. - In an official demo, Ring-2.5-1T developed a miniature operating system, further proving its capabilities in system-level programming [31]. Group 5: Broader AI Developments - Ant Group also released the diffusion language model LLaDA2.1 and the multimodal model Ming-flash-omni-2.0, which significantly enhance inference speed and provide unique token editing and reverse reasoning capabilities [33][36]. - The goal is to create a reusable foundation for developers, allowing for easier access to multimodal applications without the need to piece together various models [39][40]. - The company aims to tackle complex challenges in video temporal understanding, intricate image editing, and real-time long audio generation, indicating a commitment to advancing multimodal AI technology [41].