Core Insights - DeepSeek has launched a new model, DeepSeekMath-V2, which emphasizes self-verifiable mathematical reasoning, addressing limitations in previous AI models that focused solely on final answers [1][8][30]. Group 1: Model Capabilities - DeepSeekMath-V2 can not only provide answers but also self-check its problem-solving steps, allowing it to identify and correct its own mistakes [3][49]. - The model has achieved performance levels comparable to Olympic gold medalists, excelling in competitions such as IMO 2025 and Putnam 2024 [5][6][50]. Group 2: Philosophical Context - The model's development responds to concerns raised by AI experts about the gap between AI performance in assessments and real-world problem-solving capabilities [12][26]. - The approach taken by DeepSeekMath-V2 reflects a shift from external validation to internal self-assessment, promoting a deeper understanding of mathematical reasoning [50]. Group 3: Methodology - DeepSeekMath-V2 employs a dual-structure system with a Generator that creates solutions and a Verifier that critically evaluates these solutions for logical consistency and accuracy [46][49]. - The introduction of a Meta-Verifier ensures that the evaluation process is fair and accurate, enhancing the overall reliability of the model [49]. Group 4: Performance Metrics - In the IMO competition, DeepSeekMath-V2 solved 5 out of 6 problems, demonstrating its high-level capabilities [50]. - In the Putnam Competition, it scored 118 out of 120, showcasing its ability to tackle extremely challenging mathematical problems [50].