宇宙尺度压缩：Scaling Law的边界，柏拉图表征收敛于物质和信息交汇，解决P与NP问题，Simulation假说……

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].