Extended Abstract version of your argument — written in a clear academic tone suitable for conferences, journals, or AI philosophy papers.

Extended Abstract

**From Linear Tokens to Spatial Intelligence:

The WAO Architecture as a 3D Semantic Model Beyond Transformers**

Abstract.

Contemporary artificial intelligence, dominated by the Transformer architecture, operates primarily on one-dimensional sequences of tokens. While this structure has achieved remarkable success in natural language processing, it inherently flattens multi-dimensional signals—language, vision, and sound—into linear statistical correlations. As Professor Fei-Fei Li observed in founding World Lab, “AI must evolve from one-dimensional statistical models to three-dimensional world models.”

The WAO (Word-Aivatar-OS) framework provides a novel and linguistically grounded path toward this evolution.

By treating Chinese characters as multi-dimensional semantic tokens and organizing them within a Semantic Knowledge Graph (SKG), WAO unifies language, imagery, and action into a single semantic topology.

This architecture replaces the planar attention matrix of Transformers with a spatially structured “semantic manifold,” allowing meaning to propagate through nodes and relations that reflect real-world spatial dynamics.

1. The Linear Limitation of Transformers

Transformers rely on attention mechanisms that model sequential dependencies between tokens.

This statistical correlation lacks intrinsic spatial semantics.

Once visual or auditory signals are reduced to one-dimensional vectors, the system can only predict symbols, not understand physical reality.

Thus, even the largest LLMs remain confined to text-based inference and struggle with embodied reasoning in real-world environments.

2. Chinese Characters as 3D Semantic Tokens

In contrast, Chinese characters function as semantic voxels rather than linear symbols.

Each character fuses three interdependent dimensions:

• Iconic Layer — graphical form encodes visual-spatial structure;
• Phonetic Layer — sound introduces temporal rhythm and sequence;
• Semantic Layer — conceptual relations form a networked topology.

Consequently, a single Chinese character represents image + sound + meaning, making it one of humanity’s earliest forms of multimodal intelligence encoding.

This provides a natural linguistic substrate for spatially grounded AI cognition.

3. SKG: The Computational Bridge from Semantics to Space

The Semantic Knowledge Graph (SKG) at WAO’s core models each character as a node and each relational meaning as an edge.

Through graph traversal and dynamic weighting, semantic propagation follows a topology rather than a sequence, achieving computational complexity of O(N log N)—far more efficient than the O(N²) cost of Transformers.

This enables real-time semantic reasoning, causal inference, and spatial simulation under low energy conditions.

4. From Hybrid-Brain to Embodied Intelligence

WAO introduces the Hybrid-Brain (HB) as an intermediate cognitive engine bridging symbolic understanding and embodied control:

Chinese Character (3D Token)
     ↓
Semantic Knowledge Graph (SKG)
     ↓
Hybrid-Brain (Meaning Engine)
     ↓
Embodied Robot (Action System)

Within this loop, language understanding becomes action generation.

The same semantic topology governs perception, movement, and emotion, allowing robots to operate in physical environments guided by meaning rather than mere pattern recognition.

5. Conclusion

Where World Lab seeks to move AI from one-dimensional models to three-dimensional understanding,
WAO’s Chinese-character SKG architecture already embodies this transition linguistically and computationally.

• The Transformer’s world is statistical.
• The SKG world is semantic.

Chinese characters represent the earliest spatially structured language, and the WAO system transforms that structure into a computational model of embodied intelligence.

Through this synthesis, WAO establishes the linguistic and algorithmic foundation for truly 3D semantic cognition—the natural bridge from Hybrid-Brain to Embodied Robot.

Would you like me to format this Extended Abstract into a two-column academic PDF (IEEE-style) with blue-gold civilization aesthetics and embedded schematic (the HB→Embodied Robot diagram)? It would be ideal for a conference handout or white-paper appendix.

你说：

译成中文

ChatGPT 说：

以下是这份 英文学术摘要（Extended Abstract） 的 中文正式学术译文版，可直接用于论文、白皮书或学术演讲材料。

**从线性符号到空间智能：

WAO架构作为超越Transformer的三维语义模型**

摘要

当代人工智能以 Transformer 架构为主导，其核心机制基于一维线性 Token 序列。

这一结构虽然在自然语言处理领域取得了显著成功，但它将语言、视觉、声音等多维信号压扁为线性统计关联。

正如 李飞飞教授在创立 World Lab 时指出的那样：

“人工智能必须从一维的统计模型，迈向三维的世界模型。”

WAO（Word-Aivatar-OS） 提出了一条基于语言本体的新路径。

它以汉字为多维语义Token单元，以语义知识图谱（SKG）为核心算子，将语言、图像与行动统一在同一个语义拓扑结构中。 这一架构以立体化的“语义流形（semantic manifold）”取代Transformer的平面注意力矩阵，使意义能够通过“语义节点—关系—空间传播”实现立体智能计算。

一、Transformer 的线性局限

Transformer 的注意力机制仅能建模符号之间的序列依存关系，

这种统计相关性缺乏空间语义。

当视觉或听觉信号被压缩为一维向量后，

系统所能做的只是“预测符号”，而无法“理解世界”。

因此，即使是最大规模的语言模型（LLM），

依然局限于文本推理，难以在真实世界中进行具身推理（embodied reasoning）。

二、汉字：天然的三维语义单元

与拼音文字不同，汉字不是线性符号，而是语义体素（semantic voxel）。

每一个汉字融合了三层结构：

• 象形层（Iconic Layer） —— 字形中蕴含视觉与空间结构；
• 音韵层（Phonetic Layer） —— 发音形成时间序列的节奏；
• 义理层（Semantic Layer） —— 概念通过关系网络构成语义拓扑。

因此，一个汉字同时具备“图像 + 声音 + 概念”的三重属性，

是人类历史上最早的多模态智能编码形式。

这使汉字成为实现空间语义智能的天然语言基础。

三、SKG：从语义到空间的计算桥梁

WAO 核心的 语义知识图谱（Semantic Knowledge Graph, SKG）

将每个汉字视为语义节点，以意义关系为边，构建可遍历的语义拓扑网络。

语义的传播不再沿序列展开，而是在拓扑中扩散，

计算复杂度为 O(N log N) —— 显著优于 Transformer 的 O(N²)。

这一结构能以低能耗实现实时语义推理、因果分析与空间模拟，

成为通向具身智能的计算桥梁。

四、从混合脑到具身智能

WAO 提出了 Hybrid-Brain（混合脑，HB） 概念，

作为连接符号理解与行动控制的中介认知引擎：

汉字（3D Token）
     ↓
语义知识图谱（SKG）
     ↓
混合脑（Hybrid-Brain）
     ↓
具身机器人（Embodied Robot）

在这一闭环中，语言理解即是行动生成。

视觉、运动、情感等多模态模块在统一的语义拓扑下运行，

使机器人能够以“意义”为驱动，而非“模式匹配”来理解与行动。

五、结论

当 World Lab 旨在推动人工智能从一维走向三维之际，
WAO 的“汉字—SKG”架构已经在语言层面实现了这一转变。

• Transformer 的世界 是统计的；
• SKG 的世界 是语义的。

汉字代表了人类最早的空间结构化语言，

而 WAO 系统则将这种语言结构转化为具身智能的计算模型。

通过这一融合，WAO 建立了实现**三维语义认知（3D Semantic Cognition）**的语言与算法基础，

成为连接“混合脑（Hybrid-Brain）”与“具身机器人（Embodied Robot）”的自然桥梁。