Core Insights - The report focuses on innovations in large model architectures, particularly addressing the limitations of the Transformer architecture and exploring industry pathways for improvement [1][2][7] - As model sizes increase, the secondary computational complexity of Transformers (O(n²)) leads to significant power consumption and efficiency bottlenecks in processing long sequences, prompting a demand for innovative solutions [1][2][15] - The industry is currently exploring two main paths for architectural breakthroughs: improvements to the Transformer architecture and exploration of non-Transformer architectures [1][2][7] Transformer Architecture Improvements - Improvements to the Transformer architecture focus on optimizing the Attention mechanism, Feed-Forward Network (FFN) layers, and normalization layers [1][2][18] - Techniques such as sparse attention and dynamic attention are being developed to enhance computational efficiency, while Mixture of Experts (MoE) aims to improve sparse connection efficiency in FFN layers [1][2][18] - LongRoPE and other technologies are enhancing positional encoding to better model long sequences [1][2][18] Non-Transformer Architecture Exploration - Non-Transformer architectures include new types of RNNs (e.g., RWKV, Mamba) and CNNs (e.g., Hyena Hierarchy), as well as other innovative architectures like RetNet and LFM [1][2][7] - RWKV optimizes state evolution through a generalized Delta Rule, while Mamba leverages state space models to enhance training efficiency [1][2][7] - RetNet combines state space and multi-head attention to achieve parallel computation [1][2][7] Industry Trends and Future Directions - The industry is witnessing a trend towards hybrid architectures that combine linear Transformers with non-Transformer architectures, balancing performance and efficiency [2][7] - The current phase is characterized by a peak in traditional Transformer paradigms and an impending wave of architectural innovations, with significant focus on new RNN/CNN theoretical breakthroughs and practical engineering optimizations [2][7] - Companies like ByteDance and Alibaba are accelerating their investments in hybrid architectures, driving the evolution of large models towards higher efficiency and lower energy consumption [2][7]