Core Insights - The article discusses the advancements made by the company 超越对称 (Super Symmetry) with their new model BigBang-Proton, which integrates various scientific disciplines and challenges existing AGI approaches [1][2][4]. Group 1: BigBang-Proton Model Innovations - BigBang-Proton successfully unifies multiple scientific problems across different scales, from micro-particles to macro-earth systems, using a next-word prediction paradigm [1]. - The model introduces three fundamental innovations: Binary Patch Encoding, a theory-experiment learning paradigm, and Monte Carlo Attention, which enhances its ability to handle complex scientific tasks [9][12][16]. - The model's pre-training is designed to extend to the entire universe, proposing a concept called "Universe Compression" to consolidate vast amounts of information into a single foundation [5]. Group 2: Performance and Comparisons - BigBang-Proton demonstrates superior performance in arithmetic operations, achieving 100% accuracy in 50-digit addition, significantly outperforming other models like DeepSeek-R1 and ChatGPT-o1 [31][36]. - In particle jet classification tasks, BigBang-Proton achieved an accuracy of 51.29%, competing closely with specialized models, while mainstream LLMs performed poorly [42][44]. - The model also excels in predicting water quality and genomic sequences, achieving competitive results against state-of-the-art models in these domains [59][62]. Group 3: Theoretical and Practical Implications - The introduction of Binary Patch Encoding addresses the limitations of traditional tokenizers, allowing for better numerical analysis and integration of scientific data [11][13]. - The theory-experiment learning paradigm bridges the gap between theoretical knowledge and experimental data, enhancing the model's applicability in real-world scientific research [12][15]. - The advancements made by BigBang-Proton could significantly impact fields reliant on numerical calculations, such as science, engineering, and finance, by resolving long-standing issues related to arithmetic logic [37].