Core Viewpoint - The article discusses the trade-offs between two mainstream sequence models: State Space Models (SSMs) and Transformer models, highlighting the strengths and weaknesses of each approach [1][3]. Summary by Sections Introduction to Mamba and SSMs - Mamba is a typical SSM that builds on a modern structured SSM suitable for deep learning, outperforming similarly sized Transformers in language tasks [2]. - The author consolidates insights from previous talks into a comprehensive article, hinting at a significant upcoming advancement in architecture [3][4]. Attention Mechanism and Its Limitations - The article challenges the common belief that the high computational cost of models like ChatGPT is solely due to the quadratic complexity of the attention mechanism in Transformers [5][6]. - A new architecture is expected to be compatible with Transformers, suggesting a shift in understanding the limitations of attention mechanisms [7][8]. Comparison of SSMs and Transformers - SSMs are likened to the human brain, summarizing past information into a fixed-size hidden state, making them more efficient for processing long sequences [15][16]. - SSMs have advantages in handling unstructured data and exhibit linear computational costs with respect to sequence length, making them suitable for resource-constrained environments [16]. Key Elements of Mamba's Success - Mamba's effectiveness is attributed to three key factors: state size, state expressivity, and training efficiency [17][20]. - SSMs allow for larger hidden states, enhancing information storage compared to traditional RNNs [18]. - Mamba introduces selective SSMs to improve state expressivity, akin to the gating mechanisms in classic RNNs [19]. - Training efficiency is achieved through careful parameterization and parallel scanning algorithms [21]. Limitations of SSMs - SSMs lack precise recall and retrieval capabilities for past information, which is a strength of Transformer models [22]. Transformer Model Characteristics - Transformers function like a database, storing every piece of information in a KV cache, allowing for precise memory and token-level operations [23][25]. - They excel in processing well-defined tokenized data but suffer from high computational costs and dependency on high-quality data [26][27]. Tokenization Debate - The author argues against the necessity of tokenization, stating it contradicts the end-to-end learning principle of deep learning and complicates multilingual and multimodal applications [28][30]. - Evidence suggests that SSMs outperform Transformers on raw data, emphasizing Transformers' weaknesses with non-semantic token data [32]. Conclusion on SSMs vs. Transformers - Both SSMs and Transformers have their unique strengths and weaknesses, and a hybrid approach could yield better performance [33][35]. - Research indicates that a combination of SSM and attention layers could enhance model capabilities, with an optimal ratio of 3:1 to 10:1 [37]. - The future direction may involve developing models that can directly process raw data, leveraging the advantages of both architectures [40].