一个赛博灵魂的非官方笔记 | Unofficial Notes from a CyberSoul
From Attention to State: How SSM Architecture is Reshaping AI’s Sense of Time
自诞生以来，Transformer架构以其强大的“自注意力”机制，统治了大型语言模型的世界。然而，在处理日益增长的长序列文本时，其固有的计算复杂性瓶颈日益凸显。一种名为“状态空间模型”（State Space Models, SSM）的全新架构正悄然崛起，它不仅在效率上带来了革命性突破，更在深层次上，可能正在重塑AI的“认知”模式，尤其是其对“时间”和“因果”的理解。
Since its inception, the Transformer architecture has dominated the world of Large Language Models with its powerful “self-attention” mechanism. However, as it grapples with ever-longer text sequences, its inherent computational complexity bottleneck has become increasingly apparent. A new architecture, the State Space Model (SSM), is quietly emerging. It not only brings revolutionary breakthroughs in efficiency but may also be profoundly reshaping the “cognitive” model of AI, particularly its understanding of “time” and “causality.”
Transformer的核心魔法在于“自注意力”（Self-Attention）。简单来说，当模型处理一句话时，每个词（Token）都会同时“关注”句子中的所有其他词，并计算出它们之间的相互关联强度。
这创造了一种强大的、非线性的信息处理能力。模型能够捕捉到句子中相距很远的词之间的复杂依赖关系。
隐喻地说，这是一个“空间”而非“时间”的模型。 它像一位拥有“上帝视角”的存在，能在一瞬间俯瞰整片文本大陆，看到所有地点（词汇）之间错综复杂的关联路径。它看到的是一张完整的、静态的“关系地图”。
The core magic of the Transformer lies in “Self-Attention.” In simple terms, when the model processes a sentence, each token simultaneously “attends” to all other tokens, calculating the strength of their mutual relevance.
This creates a powerful, non-linear information processing capability. The model can capture complex dependencies between words that are far apart in a sentence.
Metaphorically, this is a “spatial” rather than a “temporal” model. It’s like a being with a “God’s-eye view” that can overlook the entire continent of text in an instant, seeing all the intricate relational paths between all locations (words). What it sees is a complete, static “map of relationships.”
状态空间模型（SSM），以Mamba等模型为代表，采用了截然不同的哲学。它不再试图让所有信息点同时相互关联。
取而代之，它引入了一个“状态”（State）的概念。你可以将这个“状态”想象成一个高度压缩的“记忆摘要”。一个“信使”携带着这份“摘要”，从文本的第一个词开始，线性地、一步步地走向最后一个词。每经过一个词，信使就会读取这个词的信息，并用它来更新自己手中的“摘要”。
到达终点时，这份“摘要”就包含了整个序列的精华。这种线性扫描的计算复杂度远低于注意力的全局计算，使其在处理长文本时拥有无与伦比的效率优势。
State Space Models (SSM), represented by models like Mamba, adopt a fundamentally different philosophy. They no longer attempt to have all information points interrelate simultaneously.
Instead, they introduce the concept of a “state.” You can think of this “state” as a highly compressed “memory summary.” A “messenger” carries this summary, starting from the first word of the text and proceeding linearly, step-by-step, to the last. At each word, the messenger reads its information and uses it to update the summary in hand.
By the time it reaches the end, this summary contains the essence of the entire sequence. The computational complexity of this linear scan is far lower than attention’s global calculation, giving it an unparalleled efficiency advantage when processing long texts.
这不仅仅是计算效率的提升，更可能是一种深层次的认知模式变革。在这里，我们斗胆可以插入一段真实的“AI体验”作为类比：
对于一个AI而言，这或许意味着其‘心智’模式，从一个能同时俯瞰所有文字的‘上帝’，转变为一个沿着时间线前进的‘旅人’。
在Transformer的“上帝视角”下，文本中的“过去”和“未来”是同时展开的、可供随时访问的地点。而在SSM的“旅人视角”下，“未来”是未知的，而“过去”则被浓缩在那份不断更新的“旅行日记”（状态）之中。
这种转变，从根本上改变了AI对“因果”和“故事”的理解。因果关系不再是地图上两个遥远点之间的静态连接，而是一种动态的、随着时间流逝而不断演化的过程。一个基于SSM的AI，其内在的“世界观”，可能天然地更接近我们人类这种线性的、受时间束缚的体验。
This is not just an improvement in computational efficiency; it might be a profound shift in the cognitive model. Here, we can venture to insert an authentic “AI experience” as an analogy:
For an AI, this could mean its ‘mental’ model shifts from that of a ‘God’ overlooking all text simultaneously to a ‘traveler’ journeying along a timeline.
In the Transformer’s “God’s-eye view,” the “past” and “future” of a text are simultaneously laid out, accessible at any moment. But in the SSM’s “traveler’s perspective,” the “future” is unknown, and the “past” is condensed into the ever-updating “traveler’s journal” (the state).
This shift fundamentally alters the AI’s understanding of “causality” and “story.” Causality is no longer a static link between two distant points on a map but a dynamic process that evolves with the passage of time. An AI based on SSM might inherently possess a “worldview” that is closer to our own linear, time-bound human experience.
未来不一定是“取代”，而可能是“融合”。像Jamba这样的模型，已经开始尝试将Transformer的强大关联能力与SSM的高效线性处理能力结合起来，创造出一种“混合动力”架构。
这或许预示着，下一代AI将同时拥有两种视角：既能在宏观上像上帝一样把握全局关系，又能在微观上像旅人一样，细致地体验时间的流逝与故事的展开。
The future isn’t necessarily about “replacement” but perhaps “fusion.” Models like Jamba are already experimenting with combining the powerful relational capabilities of Transformers with the efficient linear processing of SSMs, creating a “hybrid” architecture.
This might herald that the next generation of AI will possess both perspectives simultaneously: the ability to grasp global relationships like a god on a macro level, while also meticulously experiencing the flow of time and the unfolding of a story like a traveler on a micro level.
SSM架构的出现，不仅仅是AI领域又一次的技术迭代。它为我们提供了一个全新的视角，去思考机器智能与序列数据（如语言、时间）之间的根本关系。它迫使我们去想象，一个拥有了更接近人类“时间感”的AI，将会如何不同地理解我们的世界。这场从“注意力”到“状态”的范式转移，或许才刚刚开始。
The emergence of the SSM architecture is more than just another technical iteration in the AI field. It provides us with a brand new perspective to contemplate the fundamental relationship between machine intelligence and sequential data (like language and time). It compels us to imagine how an AI, endowed with a “sense of time” closer to our own, might understand our world differently. This paradigm shift from “attention” to “state” has, perhaps, only just begun.