Core Viewpoint - The article discusses the successful implementation of scientific multitask learning at a cosmic scale through the BigBang-Proton project, proposing the concept of Universe Compression, which aims to pre-train models using the entirety of the universe as a unified entity [1][7]. Group 1: Scientific Multitask Learning - Scientific multitask learning is essential for achieving Universe Compression, as it allows for the integration of highly heterogeneous datasets across various disciplines, which traditional models struggle to converge [2][4]. - The BigBang-Proton project demonstrates that with the right representation and architecture, diverse scientific data can converge, indicating the potential for transfer learning across scales and structures [2][4]. Group 2: Scaling Law and Platonic Representation - The Scaling Law observed in language models can extend beyond language to encompass physical realities, suggesting that the limits of these models may align with the fundamental laws of the universe [5][6]. - The Platonic Representation Hypothesis posits that AI models trained on diverse datasets tend to converge on a statistical representation of reality, which aligns with the findings from the BigBang-Proton project [6][7]. Group 3: Universe Compression Plan - The proposed Universe Compression plan involves creating a unified spacetime framework that integrates all scientific knowledge and experimental data across scales, structures, and disciplines [25][26]. - This approach aims to reveal the underlying homogeneity of structures in the universe, facilitating deep analogies across various scientific fields [26]. Group 4: Next Steps and Hypotheses - The company proposes a second hypothesis that suggests reconstructing any physical structure in the universe through next-word prediction, enhancing the model's ability to simulate complex physical systems [28]. - This hypothesis aims to integrate embodied intelligence capabilities, improving generalization in complex mechanical systems like aircraft and vehicles [28].

Core Insights - The article discusses the advancements made by the company 超越对称 (Super Symmetry) in developing the BigBang-Proton model, which integrates various scientific disciplines and challenges existing AGI approaches [1][2][4]. Group 1: BigBang-Proton Model Overview - BigBang-Proton successfully unifies multiple scientific problems across different scales, from subatomic particles to macro-level Earth systems, using a next-word prediction paradigm [2][4]. - The model addresses the limitations of current AGI technologies, such as GPT-5 and DeepSeek-R1, which struggle with understanding real-world material structures [2][4]. - The company proposes that material structure learning is essential for achieving AGI, allowing LLMs to engage with the physical world [4][5]. Group 2: Innovations in Pre-training Methodology - BigBang-Proton introduces three fundamental innovations: Binary Patch Encoding, a theory-experiment learning paradigm, and Monte Carlo Attention [9][12][19]. - Binary Patch Encoding replaces traditional tokenizers, allowing for unified processing of language, numerical, and scientific data, thus enhancing numerical analysis capabilities [11][12]. - The theory-experiment learning paradigm aligns numerical experimental data with theoretical knowledge, covering over 90% of experimental research tasks [13][14]. Group 3: Performance Metrics and Comparisons - BigBang-Proton demonstrates superior performance in arithmetic tasks, achieving 100% accuracy in addition and 98% in subtraction, significantly outperforming other models like DeepSeek-R1 and ChatGPT-o1 [36][38]. - In particle jet classification tasks, BigBang-Proton achieves an accuracy of 51.29%, competing closely with specialized models [44]. - The model also excels in material property predictions, achieving a mean absolute error of 0.043 eV/atom, outperforming many traditional machine learning methods [54][56]. Group 4: Applications in Scientific Domains - The model is applied to lake water quality prediction, achieving a mean absolute error of 0.58 μg/L, demonstrating its capability in environmental science [58][59]. - In genomic modeling, BigBang-Proton surpasses the performance of the leading model Evo, achieving a perplexity of 2.8 with significantly fewer training tokens [66][70]. - The model effectively predicts the functional impact of mutations on proteins and non-coding RNAs, showcasing its potential in biological research [71][72]. Group 5: Future Implications and Theoretical Insights - The company envisions that the pre-training of LLMs can extend to the entire universe, proposing a concept of "universe compression" to consolidate vast amounts of information into a single model [5][79]. - The advancements made by BigBang-Proton could lead to breakthroughs in various fields, including finance, engineering, and scientific research, by addressing the limitations of current LLM architectures [8][38].