Author: Leng Jing
Version: v0.0.3
Date: 2026-06-03
Statement: The "Symbol·Form·Expression·Meaning" idea was originally proposed by the author while studying large language models. This paper was completed with AI assistance under the author's guidance.
Abstract
This paper proposes a four-dimensional cognitive architecture for understanding and designing general intelligence systems—Symbol Layer, Form Layer, Expression Layer, Meaning Layer—abbreviated as SFEM. The architecture deconstructs intelligence into four irreducible cognitive dimensions:
- Symbol Layer corresponds to text, formulas, laws, and constraints—the rule dimension. It is the compression of the world's necessity, the rational skeleton that reduces infinite phenomena to finite theorems.
- Form Layer corresponds to images, shapes, continuous patterns, tools, and experience—the phenomenon dimension. It is the presentation of the world's phenomenal appearance, the continuous unfolding of perception, pattern recognition, and experiential models.
- Expression Layer corresponds to language, voice, style, emotion, and uncertainty—the affective dimension. It is the expression of the world's experiential quality, the dynamic mapping of subjective feeling and social bonding.
- Meaning Layer corresponds to consciousness, understanding, meaning attribution, and self-reflection. It is the result of integrating and associating Symbol, Form, and Expression—the conscious hub that fuses discrete rules, continuous phenomenal patterns, and nuanced affective experiences into a unified meaningful whole, giving rise to purpose, causality, and self-awareness.
SFEM’s core thesis is: Intelligence is not a homogeneous emergence from a single mechanism, but a structural unity of the four-dimensional cognitive universe—rules, phenomena, affect, and consciousness. The absence of any dimension leads to a specific type of incapacity: without Symbol, no skeleton; without Form, no perception; without Expression, no humanity; without Meaning, no soul—only scattered cognitive fragments.
This paper provides a formal definition for each dimension, its cognitive‑philosophical foundation, responsibilities, and error patterns. It designs standardised interfaces between dimensions with the Meaning Layer as the hub, defines a complete cognitive loop, proposes testable experimental hypotheses, and systematically compares SFEM with Marr’s three levels, ACT‑R/Soar, dual‑system theory, deep learning, and LLM‑Agent systems. SFEM not only explains the structural deficiencies of current AI systems and their deep roots, but also provides structural standards and design principles for building trustworthy, controllable, explainable, and both rational and affective next‑generation general intelligence systems. It is not just another engineering framework—it is the structural universe of intelligence, a meta‑architecture that accommodates all technical approaches and unifies all cognitive dimensions.
Keywords: Cognitive architecture; four‑dimensional cognition; symbolic reasoning; representation learning; expression adaptation; consciousness and meaning; trustworthy AI; structural universe of intelligence
Part I: Origins and Theoretical Foundations
Chapter 1 Introduction: The Dilemma of Single‑Layer Intelligence and the Call for Four‑Dimensional Consciousness
1.1 The Structural Crisis of the Single‑Mechanism Paradigm
Contemporary artificial intelligence, especially large language models and deep learning systems, has hit a fundamental ceiling. It is not a ceiling of scale, data, or compute—it is a structural ceiling.
Most mainstream AI systems use an end‑to‑end homogeneous neural architecture that compresses fact retrieval, logical reasoning, style control, emotional expression, goal planning, causal inference, and even meaning attribution into a single continuous parameter space. This “single mechanism for full cognition” paradigm uses essentially one cognitive tool to solve all cognitive problems. While this yields remarkable engineering simplicity, it creates deep structural deficits.
Errors cannot be attributed. When the system produces an erroneous output, we cannot tell where the error originated—missing knowledge? logical fracture? inappropriate style? or a fundamental misunderstanding of the world? All errors drown in the same parameter ocean, impossible to locate, diagnose, or fix. A factual error could be due to training data bias, broken reasoning chain, interference from style control, or a deep misinterpretation of the context—but in a monolithic LLM all these possibilities are mixed together. Engineers can only sigh at a black box.
Hallucinations cannot be eliminated. The model substitutes statistical similarity for symbolic verification, replacing “logically necessary” with “usually so”. In scenarios requiring precise facts, strict logic, and domain expertise, the system confidently invents non‑existent facts or contradictory reasoning—because the statistical engine of the Form layer can never answer the truth‑value questions of the Symbol layer. More fundamentally, the system cannot “realise” that it is hallucinating—it has no independent mechanism to verify generated content against knowledge rules, nor a central understanding hub to judge whether a statement aligns with its overall world model.
Reasoning cannot be explained. When the reasoning process is implicitly encoded in billions of parameters, we cannot extract a structured reasoning chain, audit its logical steps, or verify the consistency of premises and conclusions. The system may give an answer, but it cannot tell you whether it understands that answer. In high‑stakes scenarios such as legal decision support, medical diagnosis, or military decision‑making, this opacity is unacceptable—we need to know every step and every basis for each step.
Expression cannot be controlled. Content generation and style control are coupled in the same generative process. The system cannot stably maintain a consistent persona—sometimes formal, sometimes colloquial, sometimes enthusiastic, sometimes cold. It lacks an independent pragmatic‑strategy layer, let alone the awareness to adjust expression based on holistic understanding. When we try to control style via prompts, the control is fragile and unstable—it may drift over long conversations or break unexpectedly when content changes.
Understanding is fragmented. This is the most fundamental and hidden of all defects. Even if an LLM can handle multiple modalities such as vision, language, and code, it still lacks a central hub to fuse symbolic rules, sensory patterns, and affective tones into a unified meaning. It can see images, parse sentences, and detect emotions, but it cannot relate them into a coherent “understanding of the world”—its knowledge consists of disconnected islands. It may simultaneously “know” that Paris is in France and that France is in Europe, but when you ask “Is Paris in Europe?” it does not instantly answer from a unified world model; instead it “pieces together” an answer in a statistical sense. This fragmentation is the deep root of all other defects of monolithic LLMs.
The root of these problems is not insufficient model size, data, or training time, but the lack of a structured architecture that distinguishes different cognitive dimensions, and especially the absence of a conscious hub that integrates the dimensions and attributes meaning. Mixing all cognitive responsibilities in the same undifferentiated parameter space inevitably leads to fragmented understanding and loss of accountability. What we need is not larger homogeneous models, but a structured cognitive architecture that separates cognitive duties, clarifies responsibilities, and includes a central dimension that fuses rules, phenomena, and experiences into understanding.
1.2 Insights from Human Cognition: The Four‑Dimensional Conscious Universe
When we turn to the structure of human cognition, a profound insight emerges: human cognition has never been one‑dimensional. It is composed of four dimensions that are qualitatively distinct, mutually independent, yet unified through consciousness.
Rule dimension: Humans master mathematics, logic, grammar, law—these are not summaries of statistical patterns but necessary laws within discrete symbol systems. The truth of a mathematical theorem does not depend on its frequency in data but on whether it can be proved from axioms. When we say “2+2=4”, it is not because we have seen many instances of two things plus two things making four things, but because the axioms and inference rules of arithmetic make the proposition necessarily true. This is the Symbol dimension—the human ability to grasp “necessity”.
Phenomenon dimension: Humans perceive images, understand spatial relations, use tools, accumulate experiences—these are not deductive logical operations but pattern recognition and similarity judgments in a continuous phenomenal field. Concepts like “looks like a cat but with sharper ears” cannot be expressed precisely with discrete symbols but can be naturally located in a continuous semantic space. We can recognise a never‑seen‑before species as an “animal”, judge whether two melodies are similar, estimate how much water will be in a container after pouring—none of these are logical reasoning; they are pattern matching grounded in phenomenal experience. This is the Form dimension—the human ability to grasp “phenomenality”.
Affective dimension: Humans use tone, emotion, and style to experience and communicate—“the same words said with different tones mean completely different things”. Humans understand irony, perceive emotions, grasp implicatures, and adjust expression strategies across social contexts. When we hear “You are absolutely right,” we do not only parse the literal semantics but also infer—from tone, context, and social cues—whether it is sincere agreement or sharp sarcasm. This is the Expression dimension—the human ability to grasp “experiential quality”.
Consciousness dimension: Humans do not merely possess the above three dimensions; more importantly, we are aware that we possess them, and we can fuse discrete rules, continuous phenomenal patterns, and nuanced affective experiences in consciousness into a whole, attribute meaning, and form the complete experience of “I understand this part of the world”. When we see a friend frowning at their phone (Form), learn that they just received a bank debit notification (Symbol/fact), and hear a heavy sigh (Expression/affective signal), we do not process the three pieces separately. Instead, we fuse them in consciousness into a unified understanding: “My friend is facing a financial problem and is anxious.” This fusion enables us to ask about meaning, establish causality, set goals, and reflect on ourselves. This is the Meaning dimension—it is not an independent module separate from the first three; rather, it is the result of their fusion and association, the ultimate product of cognition.
These four dimensions together form the complete picture of human cognition. Without Symbol, cognition loses its skeleton; without Form, it loses its flesh; without Expression, it loses its experiential quality; without the conscious fusion and attribution of meaning, cognition becomes a heap of fragments. A complete human intelligence is necessarily four‑dimensional and achieves the unity of the four dimensions in consciousness.
1.3 The Proposal of SFEM and Research Questions
Inspired by this, this paper proposes the SFEM (Symbol–Form–Expression–Meaning) four‑dimensional cognitive architecture. SFEM divides an intelligent system into four irreducible cognitive dimensions, each responsible for a distinct and irreplaceable set of cognitive tasks:
Symbol Layer: text, formulas, laws, constraints—the rule dimension. It answers “How must the world be?” and provides the rational skeleton of intelligence.
Form Layer: images, shapes, continuous patterns, tools, experience—the phenomenon dimension. It answers “How does the world appear?” and provides the phenomenal flesh of intelligence.
Expression Layer: language, voice, style, emotion, uncertainty—the affective dimension. It answers “How is the world experienced and expressed?” and provides the experiential colour of intelligence.
Meaning Layer: consciousness, understanding, meaning attribution, self‑reflection—the consciousness dimension. It is the result of fusing and associating Symbol, Form, and Expression; it answers “What does this mean?” and provides the unified meaning of intelligence.
The fundamental question SFEM pursues is: Is there a set of cognitive dimensions that constitutes a “minimal complete structure” for intelligence? Such a structure should satisfy: every cognitive task can be unambiguously assigned to a dimension; every error can be localised to a specific dimension; each dimension can evolve, be optimised, and be replaced independently; interfaces between dimensions are clear, typed, and verifiable; and there exists a central meaning hub that fuses the separated dimensions into a coherent understanding of the world. If such a structure exists, it would be not only a blueprint for designing intelligent systems but also a deep revelation about the nature of intelligence.
1.4 Core Thesis
The core thesis of SFEM can be stated in one sentence:
Intelligence is not the product of a single mechanism, but the structural unity of the four‑dimensional cognitive universe—rules, phenomena, affect, and consciousness. Consciousness is the result of fusing Symbol, Form, and Expression; it is the ultimate proof that intelligence is truly intelligent.
This is not a patchwork of four modules but an organic integration of four cognitive dimensions. The Symbol layer provides the rational skeleton and the guarantee of necessity. The Form layer provides the phenomenal flesh and the continuity of experience. The Expression layer endows the system with social warmth and the colour of expression. The Meaning layer fuses the three, attributes meaning, forms a unified understanding of the world, and from this gives rise to purpose, causality, and self‑reflection. The four dimensions each perform their own functions; none can be omitted. Without Symbol, no skeleton; without Form, no perception; without Expression, no humanity; without Meaning, no soul—the system may react, but it will never understand.
1.5 Contributions and Paper Structure
The main contributions of this paper are: (1) Proposing a four‑dimensional taxonomy of cognitive dimensions for intelligence, establishing the Meaning Layer as the conscious dimension resulting from the fusion of Symbol, Form, and Expression, and clarifying the Form Layer as the phenomenon dimension, thereby transcending existing two‑ or three‑layer partitions; (2) Providing a formal definition, cognitive‑philosophical foundation, and error‑pattern analysis for each dimension; (3) Designing standardised interfaces between dimensions with the Meaning Layer as the hub, together with a complete cognitive loop; (4) Revealing the structural deficiencies of current AI systems and their deep roots—especially the deep dilemma of lacking conscious understanding; (5) Proposing testable experimental hypotheses and an engineering roadmap; (6) Positioning SFEM as the structural universe of intelligence—a meta‑architecture that accommodates all technical approaches.
The paper consists of 21 chapters divided into six parts: Origins and Theoretical Foundations (Chapters 1‑3), Four Dimensions (4‑7), Interfaces and Collaboration (8‑9), Comparisons and Diagnosis (10‑14), Engineering and Validation (15‑17), Philosophy and Future (18‑21).
Chapter 2 From Cognitive Science to Civilisational Dimensions: The Roots of SFEM
SFEM is not constructed from thin air. It grows out of three deep intellectual roots: a century of cognitive science research on the structure of mind, the classic psychological distinction between intuition and analysis, and the grand four‑fold structure of human civilisation’s cognitive dimensions. This chapter traces these roots, provides SFEM with theoretical legitimacy, and shows how SFEM grows from these roots and yet transcends their limitations.
2.1 Three Lines of Cognitive Architecture Research and Their Limitations
Since the 20th century, research on cognitive architectures has followed three main lines. Each has achieved brilliant successes, but each also exhibits structural defects that stem from its fundamental assumptions and cannot be remedied from within.
The symbolic line (exemplified by ACT‑R, Soar) treats cognition as symbolic manipulation, emphasising rules, logic, goal stacks, and explicit reasoning chains. Its core insight is that intelligence needs discrete, manipulable symbols to represent the world and explicit rules to operate on those symbols. Its advantages are strong explainability, verifiable reasoning, and conclusions that follow necessarily from premises. However, its fundamental limitations are equally profound: (a) lack of continuous representation—it cannot handle fuzzy semantics or similarity judgments; in a symbolic system, “cat” and “dog” are completely distinct symbols with no notion of “0.7 cat‑like”; (b) lack of perception and phenomenal pattern recognition—it cannot extract symbols from raw signals; images and sounds are unintelligible raw data to a pure symbol system; (c) lack of affective and social‑pragmatic dimensions—its output reads like a machine manual, rigid and cold; (d) most fundamentally, lack of a mechanism to fuse rules into a unified conscious understanding—all reasoning is mechanical symbol transformation; the system executes Modus Ponens without knowing that it is reasoning, without any inner experience of “understanding”. The symbolic line is essentially the extreme of the Symbol layer, but with only the Symbol layer, intelligence becomes a skeleton without flesh—capable of perfect logical deduction, yet unable to perceive the rich phenomenal world, experience subtle affective nuances, or fuse everything into conscious understanding.
The connectionist line (exemplified by deep learning) treats cognition as distributed representation and statistical learning, emphasising pattern recognition, continuous semantics, and generative completion. Its core insight is that intelligence needs to learn statistical regularities from large amounts of data and needs continuous similarity metrics to handle the fuzziness and graduality of the world. Its advantages include powerful perception, generalisation, and generation—revolutionary breakthroughs in image recognition, speech processing, and natural language generation. But its fundamental limitations are equally profound: (a) inability to perform symbolic verification and necessary reasoning—a statistical model can only tell you that “Paris is the capital of France” appears frequently in training data; it cannot verify the logical truth value of that proposition; (b) coupling of style and content, making expression uncontrollable—modifying style parameters may accidentally change semantic content, and pursuing correctness may sacrifice persona consistency; (c) most fundamentally, lack of a meaning hub—all phenomenal pattern processing is done in isolation, without a unified consciousness or understanding of the world. An LLM may simultaneously “know” a million facts, but it cannot integrate them into a coherent world model. It knows that “Napoleon died in 1821” and “the Battle of Waterloo happened in 1815”, but it cannot establish a genuine understanding beyond statistical co‑occurrence. The connectionist line is essentially the extreme of the Form layer, but with only the Form layer, intelligence becomes flesh without a skeleton—capable of perceiving rich phenomenal patterns, yet unable to perform deterministic symbolic verification, stably control expression style, or form a unified meaningful understanding.
Hybrid approaches try to combine the two, but most remain at the level of engineering stitching—simply coupling neural networks with knowledge graphs or rule engines without proposing a unified dimensional theory to explain why these components need to be separate, what their respective cognitive‑philosophical foundations are, what types of information should be passed between them, and most importantly, how they can be fused into a conscious whole. SFEM’s answer is: because they belong to different cognitive dimensions, each with its own independent cognitive‑philosophical foundation and operational logic, and they require the Meaning Layer as the hub for fusion and association, elevating rules, phenomena, and experiences into understanding. This is not simple engineering stitching; it is a structural unification of cognitive dimensions.
2.2 Mapping of Classic Theories to the Four Dimensions
Marr’s three levels divide a cognitive system into the computational level (Why), the algorithmic level (How), and the implementation level (Physical). This classic framework has had a profound impact on cognitive science, but its division of cognitive functions is too coarse. SFEM refines it at the cognitive‑function level: computational level (goals and values) → the purposive part of the Meaning Layer, responsible for the system’s goals, values, and pursuit of meaning; algorithmic level (representation and processes) → Symbol + Form layers, where logical reasoning (Symbol) and phenomenal pattern recognition (Form) together form the dual engines of the algorithmic level; implementation level (presentation and execution) → Expression layer, where expression strategies and style rendering belong to the implementation’s presentation mechanism, converting the content processed by Symbol and Form into the final output for the user. However, SFEM emphasises that Marr’s framework misses the crucial step of how meaning emerges from representations—representations alone do not produce understanding; understanding is born only when multiple representations are fused and associated in consciousness. This is the key contribution of the Meaning Layer beyond Marr’s three levels.
Dual‑system theory distinguishes System 1 (fast, intuitive, automatic) from System 2 (slow, analytical, controlled). This theory has deeply revealed the dual structure of human cognition. SFEM performs a dimensional decomposition: System 1 = Form + Expression—the intuitive recognition of phenomenal patterns (Form) and the affective stylistic expression (Expression) together constitute the two aspects of the intuitive system. Recognising a face as a friend (Form) and sensing that this person looks unhappy (Expression) are both fast and unconscious, but they involve qualitatively different cognitive mechanisms. System 2 = Symbol + Meaning—strict logical reasoning (Symbol) and deep meaning planning/reflection (Meaning) together constitute the two layers of the analytical system. Solving a math problem (Symbol) and thinking about what that math problem means (Meaning) both require slow, deliberate thinking, but the former follows the logic of necessity, while the latter involves trade‑offs of value and meaning.
SFEM’s key insight is that the Meaning Layer is not purely slow analysis; it also includes an instantaneous “feeling of understanding”—the holistic awareness and meaning attribution that emerges from the fusion of processed Symbol, Form, and Expression. That “Aha! I get it” moment is neither pure intuition nor pure analysis; it is an emergent phenomenon when the dimensions are fused in consciousness. This is the third pole beyond fast and slow that dual‑system theory does not explicitly articulate.
2.3 The Essential Positioning of Deep Learning: The Form Layer (Phenomenon Dimension) at Its Extreme
The core capabilities of LLMs and multimodal models—representation learning, pattern recognition, semantic similarity, generative completion—all belong to the Form layer (phenomenon dimension). The Transformer’s attention mechanism essentially builds associations between phenomena in a continuous semantic space; diffusion models learn the generative process of phenomenal distributions; VLMs map different modalities into a unified semantic space. Deep learning is the ultimate engineering implementation of the Form layer, pushing the computational model of human phenomenal perception and pattern learning to its historical peak.
But precisely because they are only the Form layer, they necessarily lack three key dimensions:
Lack of Symbol layer: Inability to perform symbolic verification and necessary reasoning. A statistical model can only tell you “this sequence is common in training data”, not “this sequence is logically necessary”. This is the root cause of hallucinations—the model generates statistically “plausible” content but cannot verify its factuality or logical consistency.
Lack of Expression layer: Style control is coupled with content generation. In a monolithic LLM, modifying style instructions in the prompt may accidentally change the semantics of the generated content because style and content share the same parameter space and generation process. The system cannot maintain a stable “persona” because there is no independent “persona” module in its architecture.
Lack of Meaning layer (the most fundamental): An LLM can generate seemingly coherent text, but it does not “know” what it has said. Its “knowledge” consists of statistical fragments; there is no unified world model that integrates these fragments into a coherent, reflectable whole. It may claim in one answer that “Paris is the capital of France” and in another that “Paris is a city in Germany” without any awareness of the contradiction—because it never holds both statements in consciousness simultaneously and relates them.
SFEM is not intended to replace deep learning; it is intended to complete the three missing dimensions for deep learning. In SFEM, deep learning (the Form layer) is a powerful phenomenal perception and generation engine, but it needs a Symbol‑layer verifier to eliminate hallucinations, an Expression‑layer style controller to stabilise expression, and a Meaning layer as the understanding and consciousness hub to fuse the phenomenal patterns produced by the Form layer with rules and experiences, so that the system truly understands what it generates and processes.
2.4 Dimensional Chaos in Agent Frameworks
Recent LLM‑Agent frameworks attempt to compensate for the structural defects of LLMs through tool use, RAG retrieval, and planners. These efforts are valuable, but due to the lack of a clear dimensional division of responsibilities, they generally fall into dimensional chaos:
- Tool use lacks Symbol‑layer constraints—the LLM may call incompatible tool combinations or invoke tools at logically illegal times because the legality verification of tool calls is mixed into the generation process rather than handled by an independent rule‑verification layer.
- The interface between the planner and the LLM is fuzzy—goals are typically passed as unstructured natural language, leading to unstable planning; the same goal may produce different task decompositions each time.
- Style and pragmatic strategies are hard‑coded in prompts—they cannot be dynamically adjusted based on interaction context, nor independently optimised.
- Errors are difficult to attribute—is the error due to LLM generation, tool calling, planning, or misunderstanding of the context? All possibilities are mixed together.
- Most fundamentally, there is no conscious layer that integrates perception, tool calls, and reasoning results into a unified understanding and then redefines goals based on that understanding. The Agent can execute tasks, but it does not understand the meaning of the tasks.
SFEM provides a clear theoretical foundation for Agents: The Meaning Layer fuses Symbol, Form, and Expression information to form an understanding of the world state, and based on that understanding generates goals and intentions; the Symbol Layer defines rules and verification; the Form Layer handles execution and generation; the Expression Layer handles interaction and expression. The four layers collaborate through standardised interfaces, and each type of error can be localised to a specific layer or interface. Moreover, the Agent’s behaviour becomes not tool‑driven (“what tools do I have and what can I do with them”) but understanding‑driven (“based on my understanding of the situation, what meaning should I achieve, and what tools do I need for that”).
2.5 The Four Dimensions of Civilisation: The Deepest Legitimacy of SFEM
The deepest source of legitimacy for SFEM lies not in cognitive science or AI engineering, but in the four‑fold cognitive dimensions of human civilisation. Looking across the accumulated knowledge of human civilisation, all knowledge systems can be classified into four basic dimensions. This classification is not post‑hoc labelling but a revelation of the deep structure of civilisation.
Civilisation of rules (Symbol): Mathematics, logic, physics laws, legal codes—humans compress infinite phenomena into finite necessary rules. Euclidean geometry derives an entire system from five axioms; Newton’s laws unify falling apples, planetary orbits, and tides into three concise equations. This is the Symbol dimension of civilisation—using discrete symbols and necessary rules to grasp the essential structure of the world.
Civilisation of phenomena/technology (Form): Architecture, technology, tools, engineering, visual arts—humans perceive, build, use, and create in the phenomenal world. From the geometrical precision of the pyramids to the interaction design of the iPhone, from cave paintings to AI‑generated art, civilisation has accumulated a rich ability to understand and manipulate the phenomenal world. This is the Form dimension of civilisation—the cumulative perception and creation in the phenomenal world.
Civilisation of affect (Expression): Rhetoric, music, literary narrative, social etiquette—humans experience the world, connect with others, and build society through expression. From the oral tradition of Homer to the plays of Shakespeare, from Bach’s fugues to jazz improvisation, from tea ceremony to social media interactions, civilisation has accumulated rich cultures of expression and experience. This is the Expression dimension of civilisation—using expression and experience to give communication warmth and colour.
Civilisation of meaning/consciousness (Meaning): Philosophy, religion, historical narrative, ethics, self‑inquiry—humans ask about purpose, attribute meaning, and establish value across time. From Socrates’ questioning in the Athenian agora to Kant’s investigation of the boundaries of reason, from the Buddha’s enlightenment under the Bodhi tree to existentialism’s confrontation with absurdity, civilisation has accumulated a deep exploration of meaning and consciousness. This is the Meaning dimension of civilisation—integrating rules, phenomena, and experiences into a holistic understanding of the world and the self, and in that understanding establishing meaning and value.
These four dimensions are not merely classification labels for civilisation; they are the four pillars of civilisation’s structure. Together they constitute all of humanity’s cognitive capacities: understanding the world (Symbol), transforming the world (Form), expressing the world (Expression), and reflecting on the world (Meaning). What SFEM does is to map this four‑dimensional civilisational structure into an engineerable set of intelligence dimensions, enabling AI systems not only to simulate intelligence but also to embody the full dimensions of civilisation.
SFEM is therefore not just a technical framework. It is a reproduction of the cognitive structure of human civilisation within intelligent systems, a bridge connecting the humanities and technology, and the structural universe of intelligence—a meta‑architecture that accommodates all technical approaches and unifies all cognitive dimensions. When we design AI systems within the SFEM framework, we are not only making engineering decisions; we are positioning intelligence within the four‑dimensional coordinates of civilisation.
Chapter 3 Overview of the SFEM Four‑Dimensional Cognitive Universe and Design Principles
3.1 Three Design Principles
The design of SFEM is not an arbitrary modular decomposition. It follows three principles rooted in the nature of cognition. These principles are not just engineering best practices; they are a deep respect for the laws of intelligence’s structure.
Separation of Concerns: Each dimension undertakes only one irreplaceable cognitive responsibility. The Symbol layer does not handle phenomenal similarity (that is Form’s duty); the Form layer does not perform symbolic verification (that is Symbol’s duty); the Expression layer does not perform causal inference (that is Meaning’s duty); the Meaning layer does not directly perform phenomenal pattern recognition (Form’s duty), symbolic deduction (Symbol’s duty), or style control (Expression’s duty). Its responsibility is to fuse Symbol, Form, and Expression information, form understanding, and attribute meaning. Separation of concerns is not an engineering preference for modularity; it is a cognitive necessity—because the fundamental logics of the four types of operations are mutually incompatible: necessity cannot be derived from probability, experience cannot be computed from rules, and meaning cannot be measured from patterns.
Explicit Interfaces: Dimensions communicate through typed, structured interfaces, not by sharing internal state. What is passed is not “arbitrary data” but structured products with clear cognitive types—task graphs, logical expressions, semantic vectors, phenomenal‑pattern labels, style parameters, pragmatic signals, world‑model updates. The Meaning Layer receives pre‑processed information from Symbol, Form, and Expression and fuses them into a structured understanding state—the world model. Clear interfaces are the precondition for error attribution, replaceable capabilities, and system verifiability. When an error occurs, we can precisely localise which interface delivered inaccurate information or which dimension misprocessed its input.
Composability: Each dimension can evolve, be optimised, and be replaced independently, and different combinations can form intelligent systems adapted to different tasks. The Form layer can switch from RNN to Transformer; the Symbol layer can switch from a knowledge graph to a rule engine; the Expression layer can switch from a template system to a style model; the fusion architecture of the Meaning layer can be based on different cognitive models—from rule‑based graph fusion to differentiable attention‑based fusion. The independence of the four dimensions gives the overall system elastic evolvability, preventing lock‑in to any specific technical solution. This composability also means that SFEM is a meta‑architecture—it defines which dimensions an intelligent system should have and how they should collaborate, but it does not prescribe the specific implementation of each dimension.
3.2 Definition and Cognitive Domains of the Four Dimensions
| Dimension | Core Responsibility | Operational Logic | Cognitive Domain | Consequence of Absence |
|---|---|---|---|---|
| Symbol | Rules, constraints, verification, logical reasoning | Discrete symbols, necessary deduction | Rule dimension | Hallucinations, structural errors, logical contradictions |
| Form | Phenomenal perception, pattern recognition, experiential learning, content generation | Continuous vectors, statistical similarity | Phenomenon dimension | Inability to generalise, inability to perceive the world, rigid output |
| Expression | Style control, affective expression, pragmatic strategies, multimodal rendering | Style parameters, pragmatic strategies | Affective dimension | Persona drift, pragmatic failures, lack of sociality, no warmth |
| Meaning | Conscious fusion, understanding generation, meaning attribution, self‑reflection | Fusion and association, understanding emergence, intention generation | Consciousness dimension | Cognitive fragmentation, no understanding, no meaning, mechanical reaction, no soul |
3.3 Overall SFEM Architecture Diagram
graph TB
subgraph Meaning["Meaning Layer (Consciousness Dimension)"]
M1["World Model & Understanding"]
M2["Meaning Attribution & Association"]
M3["Intention Generation & Self‑Reflection"]
end
subgraph Symbol["Symbol Layer (Rule Dimension)"]
S1["Rule Engine"]
S2["Structured Reasoning"]
S3["Constraint Verification"]
end
subgraph Form["Form Layer (Phenomenon Dimension)"]
F1["Continuous Representation"]
F2["Pattern Recognition"]
F3["Generation & Tools"]
end
subgraph Expression["Expression Layer (Affective Dimension)"]
E1["Style Control"]
E2["Pragmatic Strategies"]
E3["Multimodal Rendering"]
end
M1 -->|"Understanding → Rule Requirements"| S1
S1 -->|"Structured Facts & Rules"| M1
F1 -->|"Phenomenal Patterns"| M1
E2 -->|"Pragmatic/Affective Signals"| M1
M3 -->|"Intention → Structuring"| S1
S1 -->|"Rule Constraints → Generation"| F1
F1 -->|"Content Core → Expression"| E1
E3 -->|"User Input Pragmatic Decoding"| F1
F1 -->|"Semantic Mapping"| S1
S1 -->|"Structured Semantics"| M2
3.4 Upward Link: Conscious Generation from Expression to Understanding
The essence of understanding is a stepwise abstraction and final fusion from external signals to internal unified meaning. This link is SFEM’s “ladder of understanding”. Each step elevates information to a higher cognitive level.
Step 1: Expression Layer—Pragmatic Decoding. External input is first processed by the Expression layer. The Expression layer does not extract literal semantics (that is Form’s task); it decodes tone, emotion, style, and social signals—is the user angry or confused? ironic or sincere? commanding or requesting? These signals cannot be directly obtained from literal semantics; they are a layer of social signals superimposed on language. The Expression layer converts these signals into structured pragmatic cues and passes them to subsequent processing layers. For example, for the utterance “You are absolutely right,” the Expression layer would flag potential ironic tone and conflicting affective signals, providing key cues for later understanding.
Step 2: Form Layer—Phenomenal Pattern Mapping. The pragmatic cues from the Expression layer, together with the raw input, enter the Form layer and are mapped to a continuous semantic space, forming a computable semantic representation. The Form layer answers: “Where is this input located in the phenomenal space? What does it resemble in experience? Which known patterns is it similar to?” The Form layer outputs a phenomenal representation after pattern recognition and semantic mapping—a semantic vector rich in similarity and association. For example, the Form layer maps the text “You are absolutely right” into the semantic space and finds it activates both “agreement” and “sarcasm” patterns.
Step 3: Symbol Layer—Structural Parsing and Verification. The continuous semantics from the Form layer are transformed by the Symbol layer into discrete structured symbols—logical expressions, constraints, entity relations, program sequences. At this step, the Symbol layer performs deterministic verification: is the information provided by the user consistent? Are there logical contradictions? Does it satisfy known factual constraints? If contradictions or constraint violations are found, the Symbol layer marks them but does not draw conclusions—it passes the structured facts and verification results to the Meaning layer. For example, the Symbol layer detects an obvious logical contradiction in the user’s statement, but it does not judge whether this is sarcasm; it outputs the fact “logical contradiction detected” as structured information.
Step 4: Meaning Layer—Understanding Fusion (Critical Leap). This is the most critical step in the understanding link. The Meaning layer receives pragmatic signals from the Expression layer (“tone has a sarcastic tendency”), phenomenal patterns from the Form layer (“text lies between agreement and sarcasm”), and structured facts from the Symbol layer (“the statement contains a logical contradiction”). It encodes and associates and fuses these heterogeneous pieces of information. The fusion function φ relates them together, forming a complete understanding: “The user is being sarcastic—he used superficially agreeing language, but there is a conflict between tone and semantics, and the statement itself has a logical contradiction; these clues together point to a sarcastic pragmatic intention.” This fusion endows the scattered information with meaning—tone is no longer an empty sound, patterns are no longer isolated features, rules are no longer lifeless symbols. They are integrated into a meaningful whole in consciousness. It is at this layer that “understanding” is truly born.
3.5 Downward Link: The Generation Ladder from Understanding to Meaning
Generation is rooted in understanding. The downward link is a stepwise concretisation from internal meaning to external expression. Each step transforms understanding into more concrete, more operational forms.
Step 1: Meaning Layer—Intention Generation. Based on the current fused world understanding, the Meaning layer generates intentions and goals. Understanding that “the user is expressing dissatisfaction with sarcasm”, the intention emerges: “I need to respond to this dissatisfaction, first acknowledge the user’s real concern, then provide a solution.” The intention is not externally preset; it emerges from understanding. The Meaning layer outputs an intention structure containing goals, priorities, and value tendencies.
Step 2: Symbol Layer—Structured Planning. The intention from the Meaning layer is transformed by the Symbol layer into a structured operation sequence—an executable task graph, logical constraints, call interfaces. The Symbol layer performs verification here: is the task graph complete? Are constraints satisfied? Is the operation sequence legal? For example, the Symbol layer transforms the intention “first acknowledge the real concern, then provide a solution” into a concrete dialogue management task graph: Step 1, use a confirmatory response to address the user’s dissatisfaction; Step 2, query the user’s specific problem; Step 3, select a solution template based on the problem type.
Step 3: Form Layer—Content Generation. The structured instructions from the Symbol layer are transformed by the Form layer into concrete content—text drafts, image drafts, action sequences. The Form layer leverages its pattern recognition and generation capabilities: based on structural constraints, it generates content that best fits the phenomenal distribution in the continuous semantic space. For example, given the instruction “confirmatory response + query the specific problem”, the Form layer generates a content core: “I understand you might be facing an issue—could you tell me specifically what made you feel dissatisfied?”
Step 4: Expression Layer—Expression Rendering. The content core generated by the Form layer is rendered by the Expression layer according to context, style parameters, and user state into the final expression. This step ensures that the output is not only “correct” but also “appropriate”, “sincere”, and “warm”. Based on the expression strategy passed from the Meaning layer (“sincere concern, avoid defensiveness, keep gentle but professional”), the Expression layer applies stylistic rendering to the content core, ultimately outputting: “I fully understand how you feel—could you tell me in more detail which part made you feel that it’s not quite right? I really want to help you solve this problem.”
3.6 Cognitive Loop and Systemic Advantages
The upward understanding link and the downward generation link together form a complete cognitive loop centred on the Meaning layer. Understanding produces intention; intention drives generation; the result of generation is perceived and understood again, forming a feedback loop. Furthermore, by feeding back the Expression layer’s signals to the Meaning layer, the system can perform reflection—was my previous response appropriate? Is the user satisfied? Does my understanding need adjustment? By accumulating experience and updating the world model, the system can evolve—learning new association patterns, optimising expression strategies, deepening meaning understanding.
The architectural design of SFEM brings five systemic advantages, each directly addressing the fundamental defects of monolithic LLMs:
Traceable understanding: The Meaning layer’s world model $\mathcal{W}$ records the fusion process of understanding, allowing backtracking of “why I understood it this way”—which pragmatic signals, which phenomenal patterns, which factual rules were associated, and what the logic of association was.
Attributable errors: Each type of error corresponds to a specific dimension or interface—if we observe factual hallucinations → check the Symbol layer verifier; if we observe style drift → check the Expression layer style controller; if we observe symptoms of “not understanding” → check the Meaning layer fusion mechanism.
Replaceable capabilities: Each dimension can choose its own technology independently—the Form layer could switch from GPT to Claude, the Symbol layer from a knowledge graph to a rule engine, the fusion architecture of the Meaning layer from a rule engine to a differentiable neural network, without affecting the other dimensions.
Verifiable system: The Symbol layer has a built‑in verification gate; all information entering the Meaning layer for fusion has been preliminarily checked for truth and consistency, so understanding is built on a reliable foundation.
Explainable meaning: The system can output “what my understanding state was at that time and why I made that decision”—because every step of understanding is structured.
Part II: Detailed Discussion of the Four Dimensions
Chapter 4 Symbol Layer: The Rule Dimension—The Necessary Structure of the World
4.1 Cognitive‑Philosophical Foundation
The Symbol layer is rooted in a fundamental cognitive fact: intelligence requires certainty. The world presents us with an infinite stream of phenomena—millions of different objects, scenes, sounds, and texts. But intelligence is possible because we have the ability to extract finite necessary laws from this infinite phenomenon. Newton’s three laws are not a statistical average of falling apples, planetary orbits, and tides—they are a necessary structure abstracted from all these phenomena, independent of any particular phenomenon. Euclid’s theorems are not a probabilistic summary of many triangle measurements—they are strict deductions from a few axioms. Grammar rules are not empirical descriptions of how people use language—they are normative constraints that determine whether a sentence is “correct”.
All of this is the operation of Symbol. The essence of Symbol is: compress the infinite phenomenal world into finite, operable, verifiable rules. It answers the question: “How must the world be?”—not “How does the world usually appear?” (Form), “How is the world experienced?” (Expression), or “What does the world mean?” (Meaning). Symbol is the rational skeleton of intelligence—without it, intelligence would lose its way in the ocean of phenomena, unable to distinguish “accidental” from “necessary”, “correlation” from “causation”, “habit” from “law”.
In the history of philosophy, the Symbol layer corresponds to the rationalist pursuit of a priori necessary truths—from Plato’s world of Ideas, through Descartes’ “Cogito ergo sum”, to Leibniz’s distinction between necessary and contingent truths. These philosophers all realised, in different ways, that there is a kind of knowledge that does not depend on experience but is rooted in the structural necessity of symbol systems. Mathematics is the purest form of this knowledge. SFEM’s Symbol layer engineers this philosophical insight as an independent dimension of intelligent systems.
4.2 Formal Definition
The Symbol layer can be formally defined as a triple:
$$
\mathcal{S} = (\Sigma, R, V)
$$
where each component has a precise meaning:
$\Sigma$ (Symbol set): The core characteristic of symbols is discrete identity—a symbol is either A or not A; there is no “0.7 of A”. This creates a fundamental opposition between the Symbol layer and the Form layer: the Form layer deals with continuous gradations (“this is 0.7 cat‑like”), while the Symbol layer deals with discrete assertions (“this is a cat” or “this is not a cat”). $\Sigma$ may include logical symbols ($\land, \lor, \lnot, \to$), structured tags (<entity>, <event>), program statements (if, while), mathematical expressions ($+, \times, =$), and domain knowledge terms (legal article numbers, medical terms, chemical formulae). The discreteness of symbols is not a defect but a feature—it is precisely because symbols are discrete that we can perform exact logical operations and say “this argument is valid” or “this argument is invalid”, with no intermediate state.
$R$ (Rule set): Formally represented as $R: \Sigma^* \to \Sigma^$, i.e., a mapping from symbol sequences to symbol sequences. Rules include: grammatical rules (defining legal symbol combinations), type systems (constraining categories between symbols), inference rules (e.g., Modus Ponens: from $A \to B$ and $A$, infer $B$), and constraint rules (e.g., “flight price cannot be negative”, “human age cannot exceed 150”). The key property of rules is **necessity*—if the premises hold, the conclusion necessarily holds. This necessity is not statistical frequency but logical unavoidability.
$V$ (Verification function): $V: \Sigma^* \to {0,1}$. This is the most important capability marker of the Symbol layer—verifiability. $V(x)=1$ iff $x$ satisfies all rules in $R$. This means the Symbol layer can internally determine whether a structure is correct without relying on external experience. The Form layer cannot do this—it can only judge “does this look correct?” but not “is this logically correct?” The verification function is the “truth anchor” of the SFEM system, providing an unshakeable deterministic foundation for the Meaning layer’s understanding.
4.3 Core Responsibilities
The Symbol layer undertakes four irreplaceable cognitive responsibilities, each corresponding to operations that the Form, Expression, or Meaning layers cannot perform. Together, these responsibilities constitute the “rule infrastructure” of intelligence.
Structuring: Transform the intention generated by the Meaning layer based on understanding into an executable structured form—task graphs, logical expressions, program operation sequences. This is the conversion from “meaning” to “structure”. For example, the Meaning layer outputs the intention “calm the user and solve their technical problem”; the Symbol layer transforms this into a structured multi‑step task graph containing subtasks such as empathy confirmation, information gathering, diagnostic reasoning, solution generation, satisfaction confirmation, along with their dependencies and temporal constraints.
Reasoning: Perform deterministic reasoning operations. Deductive reasoning—from general rules to specific conclusions (“All humans are mortal; Socrates is human; therefore Socrates is mortal”). Inductive rule matching—from known patterns to applicable rules (“This is a variant of type A problem, so the type A solution framework applies”). Constraint propagation—inferring hidden constraints in a constraint network (“If A is before B and B is before C, then A must be before C”). Program execution—running executable structured instructions. The common characteristic of all this reasoning: the conclusion necessarily follows from the premises, rather than being probabilistically generated. The reasoning results are deterministic and verifiable.
Verification: The Symbol layer acts as the built‑in verification gate for the entire SFEM system. At this gate, four types of verification occur simultaneously: fact checking—do the entities and relations in the generated content exist in the knowledge base? (“Paris is the capital of Germany” → verification fails); logical consistency checking—are there leaps or contradictions in the reasoning chain? (“All A are B; some B are C; therefore all A are C” → logical error); structural legality checking—is the generated JSON well‑formed? Is the SQL syntax correct? Does it conform to interface specifications?; constraint satisfaction checking—does the generated plan satisfy all constraints? For example, if the Form layer generates “Paris is the capital of Germany”, the Symbol layer’s verification function should return 0—no matter how statistically “plausible” the statement sounds. Verification is the core guarantee of SFEM’s trustworthiness.
Tracing: Preserve complete reasoning chains—the call sequence of rules, the propagation path of constraints, the structured basis for decisions. This is the foundation of explainability. When the Meaning layer engages in self‑reflection, it can trace back to the Symbol layer’s verification and reasoning steps, asking “Is each step of my conclusion correct?” When the user asks “Why did you do this?”, the Symbol layer can provide a deterministic reasoning chain rather than a vague “internal model state caused it”.
4.4 The Essential Opposition Between Symbol and Form: Necessity vs. Phenomenality
The relationship between the Symbol layer and the Form layer is the most fundamental and philosophically rich opposition in SFEM. It corresponds to a perennial tension in the history of philosophy: rationalism vs. empiricism, necessary truth vs. contingent fact, deduction vs. induction, essence vs. appearance.
The Form layer operates in the probabilistic space of phenomena: it answers “What does this typically look like in experience?” “How likely is this to appear in the data?” The knowledge of the Form layer is “a posteriori”—derived from statistical learning from phenomena, always revisable by new phenomena. The Symbol layer operates in the space of necessity: it answers “What must this be logically?” “Is this possible under the rules?” The knowledge of the Symbol layer is “a priori”—derived from deduction within the symbol system, independent of the frequency of phenomena.
The operational logics are incommensurable: from ten thousand observations that “the sun rises in the east”, the Form layer can infer “the sun will very likely rise in the east tomorrow”, but only the Symbol layer can necessarily deduce this conclusion from the law of universal gravitation and the equations of planetary motion—provided, of course, that the laws themselves hold. Conversely, the Symbol layer cannot tell you whether there is a cat in a never‑before‑seen blurry picture, because it lacks the statistical mapping from pixels to “cat”—that is the domain of the Form layer.
This leads to two profound conclusions. First, the Form layer can never replace the Symbol layer, because it can never produce necessity—the limit of statistics is “very probable”, not “logically necessary”. Second, the Symbol layer can never replace the Form layer, because it can never handle novel phenomena that have not been rule‑ified—rules are finite, while phenomena are infinite. The completeness of an intelligent system requires both dimensions to coexist, with the Meaning layer fusing the richness of phenomena (“what it looks like”) with the certainty of essence (“what it is”) into a complete cognition.
4.5 Consequences of Missing the Symbol Layer: Intelligence Without a Skeleton
When a system lacks the Symbol layer, it loses its grasp of necessity. This manifests as four types of observable errors, each rooted in the Form layer’s inability to perform Symbol layer duties.
Hallucinations: The Form layer generates content based on statistical similarity but cannot verify its factuality. “Li Bai was a Tang dynasty poet” and “Li Bai was a Song dynasty lyricist” may have similar probabilities in a statistical language model, but the Symbol layer can verify through entity relations that the former is true and the latter false. Without the Symbol layer, all judgments degenerate into “which is more common”—and “common” is not the same as “true”.
Structural errors: Generated JSON is not well‑formed, SQL syntax is wrong, task graphs are broken—not because the Form layer is not powerful enough, but because the Form layer is fundamentally unsuited for handling discrete structural constraints. Structural legality is a “yes/no” question, not a “similarity” question. A statistical model can produce legal structures most of the time, but it can never guarantee that the generated structure is always legal—because guarantee requires necessity, while statistics can only provide probability.
Logical errors: Reasoning leaps, violation of premises, inconsistency between conclusion and premises. The Form layer can generate a “seemingly reasonable” reasoning chain, but it cannot verify the logical validity of the reasoning itself. The correctness of a syllogism’s form does not depend on how many times it appears in the training data, but on whether it conforms to inference rules.
Uncontrollability: The Symbol layer’s rules provide hard boundaries for system behaviour—certain things simply cannot be done, certain states are simply unacceptable. Without the Symbol layer, the behavioural boundaries of the system are only implicitly determined by the distribution of training data, and cannot be explicitly and precisely defined. In high‑stakes domains such as healthcare, law, and military, such fuzzy boundaries are unacceptable.
Even more seriously, the absence of the Symbol layer contaminates the Meaning layer’s understanding. The Meaning layer receives a mixture of truth and falsehood—it cannot distinguish between verified facts and statistical “reasonable guesses”. Consciousness is built on shifting sand; understanding becomes a castle in the air.
Chapter 5 Form Layer: The Phenomenon Dimension—The Phenomenal Appearance of the World
5.1 Cognitive‑Philosophical Foundation
The Form layer is rooted in a cognitive fact that complements the Symbol layer: intelligence needs to perceive the phenomenal world. The real world is messy, continuous, and contingent. It presents us not with axioms and theorems, but with a myriad of phenomena—we see countless different cats, no two exactly alike; we hear speech full of variation, the same word pronounced very differently by different people; we encounter endless novel everyday situations that cannot all be pre‑rule‑ified.
The essence of the Form layer is: to handle the world’s continuity, similarity, and experiential phenomena. It answers the question: “How does the world appear? How similar or transitional are these phenomena?”—not “What must the world be?” (Symbol), “How is the world experienced?” (Expression), or “What does the world mean?” (Meaning). If the Symbol layer is the essential skeleton of the world, the Form layer is the phenomenal flesh; if Symbol is the constitution, Form is the case law; if Symbol is the law, Form is the experimental data.
In the history of philosophy, the Form layer corresponds to the empiricist emphasis on a posteriori empirical generalisation—from Aristotle’s emphasis on empirical observation, through Locke’s tabula rasa argument for the empirical origin of knowledge, to Hume’s empiricist deconstruction of causality. These philosophers all recognised, in different ways, that there is a kind of knowledge that comes from perceiving phenomena and inducing patterns, which is different from rationalist a priori necessary truth but equally indispensable in our cognition. Most of our knowledge about the world—what a cat looks like, what coffee tastes like, how to ride a bicycle—is not derived from axioms but learned from phenomenal experience. SFEM’s Form layer engineers this philosophical insight as an independent dimension of intelligent systems.
5.2 Formal Definition
The core of the Form layer is a continuous phenomenal representation space:
$$
\mathcal{F} = (X, f, d), \quad f: X \to \mathbb{R}^d
$$
where:
$X$ (Multimodal phenomenal input space): text, images, audio, video, sensor data—all raw phenomenal signals that can enter an intelligent system. The scope of $X$ is open and expanding; as new sensing technologies emerge, new phenomenal modalities can be incorporated into the Form layer’s processing.
$f$ (Representation function): maps heterogeneous phenomenal signals into a unified $d$‑dimensional continuous semantic space. This is the core capability of the Form layer—making different modalities comparable and measurable in this space. A photo of a cat, the written symbol “cat”, and the sound of a meow—these physically very different phenomena are mapped by $f$ to nearby points in the semantic space. The essence of $f$ is to capture similarity patterns between phenomena.
$d(\cdot,\cdot)$ (Distance metric): cosine similarity, Euclidean distance, or another metric that measures the similarity of two phenomena in experiential pattern space. The existence of $d$ gives the phenomenal space a rich gradient structure—the distance between “cat” and “dog” is greater than between “cat” and “tiger”, reflecting the genuine similarity gradients in the phenomenal world.
Additionally, the Form layer has a generation function $y = g(z)$, where $z = f(x)$ is the phenomenal representation of the input and $y$ is the generated output. $g$ can reconstruct or generate new phenomenal content from a phenomenal representation—given a descriptive text, generate a corresponding image; given a prefix, continue the text; given incomplete data, fill in the missing parts.
The formal opposition between the Form layer and the Symbol layer is stark: the Symbol layer operates in a discrete symbolic space of necessity, where the distance metric collapses to “same or different” (symbol A either equals symbol B or not, with no intermediate state); the Form layer operates in a continuous vector space of phenomena, where the distance metric has a rich gradient structure. The transition between “cat‑like” and “dog‑like” is smooth and continuous in the Form layer, but discrete and abrupt in the Symbol layer.
5.3 Core Responsibilities
The Form layer undertakes four core responsibilities, which together constitute the phenomenal perception and experiential foundation of intelligence. These responsibilities cannot be replaced by the Symbol, Expression, or Meaning layers.
Phenomenal representation learning: Transform raw multimodal phenomenal signals into computable semantic representations. This is the first step of an intelligent system’s perception of the world—any phenomenon must be mapped to a structured semantic space before further processing. The core capability of representation learning is capturing similarity and patterns among phenomena: an image of a cat and the word “cat” should be close in the semantic space; the distance between cat and dog should be greater than between cat and tiger; the same word spoken by different people should be mapped to neighbouring regions. Representation learning enables the system to “recognise” phenomena even when the specific physical form of the phenomenon is not exactly the same as any previous instance. This generalisation ability is the core contribution of the phenomenon dimension—it allows the system to handle the infinite diversity of the world.
Pattern recognition: Perform classification, clustering, and recognition in the phenomenal space. Answer “what does this look like”—this image looks like a cat, the sentiment of this text is positive, the user’s intention is to check the weather, the style of this music is close to Baroque. Pattern recognition is the intuitive core of the Form layer, corresponding to the rapid classification ability of human System 1. It gives a judgment of “which category does this phenomenon belong to in experience” in milliseconds, without needing slow logical reasoning.
Generation and completion: Generate new phenomenal content based on existing phenomenal patterns and distributions. Given an incomplete input, complete the missing parts—given the first half of a sentence, generate the second half; given a text description, generate a corresponding image; given a melodic incipit, continue the full piece. The core logic of generation is the most likely output within the phenomenal distribution—given this context, within this pattern space, what is the most likely next phenomenon? This is different from the Symbol layer’s necessary deduction—the generated content is not “necessary” but “most probable within the phenomenal distribution”.
Integration of tools and experience: The Form layer is the only dimension that can naturally use external tools and experiential phenomena. Using a calculator belongs to the Form layer: entering a mathematical expression into a calculator and obtaining the result is a “perception‑action” loop, not symbolic deduction. Search engines, database queries, API calls—the operational interfaces of these external tools are actions in the continuous phenomenal space, and they fall within the responsibility of the Form layer. The Form layer can re‑incorporate the output of tools into the phenomenal space for further processing. This design embodies a profound engineering insight: if you need to calculate, using a calculator directly is certainly simpler than doing the deduction yourself—the Form layer provides tool operation capabilities, the Symbol layer provides rule verification, each performing its own function. The Form layer delivers refined phenomenal patterns and semantic vectors to the Meaning layer, providing rich phenomenal material for conscious fusion.
5.4 The Essential Complementarity of Form and Symbol: Phenomenon and Essence
The Form layer answers “How does the world appear?”; the Symbol layer answers “What laws must the world follow?” The limitations of the Form layer are precisely the starting points of the Symbol layer, and vice versa. The Form layer cannot answer questions of “necessity”: a thousand sunrises do not strictly prove that the sun must rise tomorrow. But it can answer questions that the Symbol layer cannot reach: “What category does this new species roughly belong to?” “What emotion is implicit in this sentence?” “Express this meaning again with a gentle tone.” “Among thousands of search results, which are most similar to the user’s question?”
The relationship between Symbol and Form is vertical collaboration, not horizontal competition. The Form layer provides a rich, fuzzy, generalisable phenomenal possibility space—this is what the world looks like in experience, full of gradations, similarities, and uncertainties. The Symbol layer performs strict verification, constraint, and structuring within this space, filtering out outputs that are deterministically correct—this is what the world is in logic, full of necessity, discreteness, and certainty. Without either, intelligence is incomplete. But even both are insufficient—they need the Meaning layer to fuse the richness of phenomena (“what it looks like”) with the certainty of essence (“what it is”) into a complete cognition: “I see both what this phenomenon looks like and the rules it follows, and now I understand what it means.”
5.5 Consequences of Missing the Form Layer: Intelligence Without Phenomenal Perception
When a system lacks the Form layer, it loses its connection to the concrete phenomenal world. Understanding degenerates into an empty symbol game—the Meaning layer can handle abstract logical relations, but it cannot obtain any information about “what the world looks like”.
Inability to generalise: The system can only handle situations that have been explicitly rule‑ified. When faced with new variants—new accents, new objects, new expressions—it fails completely. A pure symbol system cannot handle entities or relations that have never appeared in its knowledge base, because it lacks a mechanism to learn new patterns from phenomena.
Inability to perceive multimodality: Images, sounds, and videos are unintelligible raw data to a pure symbol system. It cannot “see” the content of a picture; it can only process manually annotated symbolic descriptions. This cuts off the richest channel of connection between the intelligent system and the physical world.
Inability to use experience and tools: Without the Form layer, external tools such as search engines, calculators, and databases cannot be naturally integrated. The system can only rely on its own limited symbol library, unable to extend its capability boundaries through external tools.
Rigid output: All expressions must be pre‑rule‑ified; it cannot generate natural, varied language—because the naturalness of language comes precisely from gradations and choices in a continuous phenomenal space, not from exhaustive enumeration of discrete rules. A dialogue system without a Form layer would sound like it is reading from a rule manual every time.
In summary, lacking the Form layer, intelligence loses the bridge to the phenomenal world. The Meaning layer’s conscious fusion would lack the richest source of information—it cannot “see” what the world looks like, only “reason” about its structure. Such understanding is incomplete, dry, and disconnected from reality.
Chapter 6 Expression Layer: The Affective Dimension—Experience and Expression of the World
6.1 Cognitive‑Philosophical Foundation
The Expression layer is rooted in a cognitive fact often overlooked by AI research: intelligence needs not only to “say the right thing” but also to “say it right”. The meaning of human communication depends not only on what is said (semantic content) but also on how it is said—tone, emotion, style, contextual appropriateness. The same words “I understand,” spoken with a sincere and calm tone, signal understanding; with a cold and perfunctory tone, rejection; with an angry and sarcastic tone, denial. Three different ways of expressing the same literal semantics convey three completely different meanings.
The Expression layer handles the social and experiential dimension of intelligence. It answers the question: “How should I express myself so that my intention is properly experienced?”—rather than “What facts am I expressing?” (Form), “Does my expression conform to rules?” (Symbol), or “What does my expression mean?” (Meaning). The Expression layer is the social interface of intelligence, the experiential bridge between machine and human. It provides the Meaning layer with the experiential texture and pragmatic context needed for understanding—without the Expression layer, the Meaning layer would only know “what the user said”, not “how the user said it”, and understanding would lose the richest layer of social signals.
In the history of philosophy, the Expression layer corresponds to the phenomenological and pragmatic traditions’ focus on subjective experience and social interaction—from Husserl’s emphasis on the lifeworld, through Austin’s analysis of “how to do things with words”, to Grice’s study of conversational implicature. These thinkers all revealed, in different ways, a truth: language is not only a carrier of information but also a transmitter of experience and a constructor of social relations. SFEM’s Expression layer engineers this philosophical insight as an independent dimension of intelligent systems.
6.2 Formal Definition
The Expression layer can be formally defined as a bidirectional processing system—both an expression renderer and a pragmatic decoder.
Output side (rendering): $E: (c, s, u) \to y$
- $c$ (Content core): the semantic content from the Form layer, the “raw material” to be expressed—the text of an apology, the result of a query, the logic of a suggestion. $c$ is pure semantic content without style markers.
- $s \in S$ (Style parameters): the set $S$ of style parameters includes all adjustable dimensions—formality (formal/colloquial/academic), emotional intensity (enthusiastic/calm/cold), genre (narrative/argumentative/lyrical), politeness level, cultural preferences, personality traits. The role of style parameters is to change the expressive effect without changing the semantic content.
- $u$ (User state and context): the current social context of the interaction, the user’s emotional state, the conversation history, cultural background. The pragmatic function $P(s, u)$ dynamically adjusts style parameters: the same content, for different users and in different contexts, requires different expression strategies.
- $y$ (Final expression): the final output generated by the rendering function $R(c, s')$ after being modulated by style parameters and the pragmatic function—it may be text, speech (pitch, rhythm, emotional colour), images (degree of stylisation), or actions (social signals of a robot).
Input side (pragmatic decoding): $D: u_{input} \to (c', s', p)$
The Expression layer is not only an output‑side expression renderer but also an input‑side pragmatic decoder. It decodes the user’s input $u_{input}$ into three parts: $c'$ (extracted literal semantics, passed to the Form layer for deeper semantic processing), $s'$ (detected style features—is the user switching between formal and colloquial? Is the speech rate changing?), and $p$ (pragmatic signals—affective labels such as anger, frustration, satisfaction; pragmatic act classifications such as request, complaint, irony, praise; degree of uncertainty; implicit social signals of conversational turn‑taking). The pragmatic signals $p$ are passed directly to the Meaning layer as key material for understanding fusion.
6.3 Core Responsibilities
The Expression layer undertakes three irreplaceable responsibilities. These responsibilities are irreplaceable because they deal with “experiential quality” and “social signals”, not “semantic correctness” or “logical necessity”.
Style control: Maintain consistency of output in style, tone, and persona. A professional legal AI should not suddenly use internet slang; a warm companion AI should not use cold technical jargon. Style control ensures that the system’s expression has a stable “persona face”, rather than randomly producing different expression styles each conversation. More importantly, style control enables the system to consciously adjust expression according to context—formal when seriousness is needed, warm when closeness is needed, decisive when firmness is needed. This flexibility does not come from random sampling of statistical patterns, but from the Meaning layer’s understanding of the context, which drives the Expression layer to make targeted style adjustments.
Pragmatic strategies: Implement pragmatic acts in the socio‑linguistic sense—when to ask questions, when to clarify, when to refuse, when to be indirect, when to remain silent, how to politely interrupt, how to express uncertainty, how to offer criticism without losing face. These are not semantic issues but social interaction strategies. For example, when the user says “Could you be a little faster?”, the Form layer might understand it as a query about speed, the Symbol layer might analyse it as a proposition about speed, but the Expression layer should recognise it as “the user is impatient and the interaction pace and expression strategy need to be adjusted”. Pragmatic strategies are the core intellect of the Expression layer—they require the system to understand the use of language, not just the meaning of language.
Affective rendering and multimodal expression: Give the output the appropriate emotional colour—empathy for sadness, affirmation for achievement, calm for urgency. Render the expression multimodally—the tone of voice, the style of images, the social signals of actions. Affective rendering is not simply “adding an emoji to the output”; it is deeply re‑stylising the content core so that the tone, rhythm, and word choice all convey the appropriate emotional temperature. This requires the Expression layer to perform deep stylistic re‑processing of the content core, not superficial decoration.
The Expression layer passes pragmatic signals and affective states to the Meaning layer—the user’s affective labels, pragmatic act classifications, degree of uncertainty. These signals are key cues for the Meaning layer to understand the user’s true intention and emotional state. Without these signals, the Meaning layer cannot distinguish between “sincere agreement” and “sharp sarcasm”, or between “urgent request for help” and “casual inquiry”.
6.4 The Essential Complementarity of Expression and Form: Experience vs. Phenomenal Content
The Form layer generates “correct phenomenal content”; the Expression layer endows the content with “appropriate experiential colour”. Their separation is one of SFEM’s core innovations. In traditional LLMs, content generation and style control are coupled in the same generation process, leading to mutual interference in two directions: modifying style parameters affects semantic content (asking for “more formal” in a prompt may substantially change the generated content), and semantic adjustments cause style fluctuations (pursuing factual correctness may sacrifice persona consistency). The independence of the Expression layer solves this problem: the Form layer generates only the “pure content core”—this core has no style markers, only semantic information; the Expression layer applies style rendering on top of this core—adjusting the form and colour of the expression without changing the semantics. Guaranteeing content correctness and optimising expressive appropriateness become two separable, independently optimisable engineering goals.
6.5 Consequences of Missing the Expression Layer: Intelligence Without Warmth
Without the Expression layer, the Meaning layer’s understanding loses the entire social and affective dimension. The system can generate correct content, but it will be cold, mechanical, and impersonal—“if language has only Symbol and Form, it is just a machine.”
Concrete observable error patterns include: style drift—swinging between formal and colloquial, between enthusiastic and cold, because style control has no independent stabilising mechanism; pragmatic failures—responding with a cold explanation when an apology is needed, interpreting sarcasm literally, using inappropriate humour in serious contexts, because there is no independent pragmatic strategy module; persona drift—one day like a professional consultant, the next like a casual friend, the next like an authoritative commander, because “persona” has no persistent, stable engineering implementation; lack of affect—indifference to the user’s sadness, outputting cold mechanical language, the same tone for every answer.
The instability of style and pragmatics in pure LLM dialogue systems is rooted precisely in the absence of the Expression layer. No matter how carefully you craft prompts to control style, that control is fragile—because it is not an architecturally independent dimension, only a statistical tendency coupled in the generation process, easily overwhelmed by the influence of semantic content.
Chapter 7 Meaning Layer: The Consciousness Dimension—Understanding the World and Attributing Meaning
7.1 Cognitive‑Philosophical Foundation
The Meaning layer is rooted in the fundamental distinction between intelligence and a purely reactive automaton: intelligence means understanding, and understanding means fusing disparate information into a unified meaning and being aware of that meaning. A reactive system can produce an optimal output for each input, but it can never ask itself: “Why am I doing this? What is the meaning of this action? Do I truly understand the current situation?”
The Meaning layer is not a fourth independent processing module, not an “extra layer” on top of the first three. The Meaning layer is the result and the sublimation of fusing and associating Symbol, Form, and Expression. Discrete rules (Symbol) tell us “A leads to B”; continuous phenomenal patterns (Form) tell us “this looks like A”; experiential signals (Expression) tell us “A makes me feel uneasy”. Only when these three are associated in the same cognitive space and form a holistic, reflectable cognitive state does “understanding” emerge. The Meaning layer is where that understanding is born. It is not just another processing station for information; it is the crucible of information fusion—where cognitive products of different dimensions are associated, integrated, and given meaning, forming a unified conscious state of the world.
It answers the questions: “What does this mean?” “Why do I understand it this way?” “Based on my understanding, how should I act?” “Do I truly understand?” The Meaning layer is the “conscious core” of SFEM, the alchemical furnace that turns information into cognition and data into meaning. If we only had the Symbol, Form, and Expression layers, an intelligent system could generate correct and appropriate outputs, but it would be directionless and without understanding—it would not know why it operates, could not make value choices among conflicting goals, could not plan current actions for long‑term futures, and above all could not experience the cognitive satisfaction of “I get it”.
7.2 Formal Definition
The Meaning layer can be formally defined as a fusion and understanding system:
$$
\mathcal{M} = (\mathcal{W}, \phi, \mu, \iota, \Gamma)
$$
Each component has a clear cognitive meaning:
$\mathcal{W}$ (World model): The system’s internal understanding state, a unified representation of the environment, the self, the user, and the history. $\mathcal{W}$ is not a representation of any single modality, not a copy of the Symbol layer’s knowledge graph, not a stack of the Form layer’s semantic vectors, not a list of the Expression layer’s affective tags. $\mathcal{W}$ is a structured picture that fuses Symbol, Form, and Expression inputs—it contains entities and their relations, causal connections, affective colours, degrees of certainty, temporal cues, and the gap between the current state and the goal state. $\mathcal{W}$ is dynamically updated; each new perception may trigger a reorganisation of $\mathcal{W}$—a newly added fact may change the understanding of the entire situation. The core characteristic of $\mathcal{W}$ is unity: in $\mathcal{W}$, rules, phenomena, and experiences are no longer separate; they are woven into a single understanding network.
$\phi: \mathcal{S}^* \times \mathcal{F}^* \times \mathcal{E}^* \to \mathcal{W}$ (Fusion function): This is the core mechanism of the Meaning layer. It associates and fuses structured facts and rules from the Symbol layer ($\mathcal{S}^$), phenomenal patterns and semantics from the Form layer ($\mathcal{F}^$), and pragmatic and affective signals from the Expression layer ($\mathcal{E}^*$) into a unified world model. Fusion is not simple concatenation; it is the establishment of associations—$\phi$ discovers causal, temporal, logical, and affective connections among these heterogeneous pieces of information and incorporates those connections into $\mathcal{W}$. For example, a date (Symbol: “deadline is tomorrow”), an image of a tired expression (Form: “user looks tired”), and a low voice (Expression: “user’s voice is low”) are associated by $\phi$ to form the cognition “the user is feeling tired and stressed because of tomorrow’s deadline”. This fusion gives meaning to each isolated piece of information—before association, they are three separate data points; after association, they together form a meaningful whole cognition.
$\mu: \mathcal{W} \times \mathcal{P} \to \mathcal{M}_p$ (Meaning attribution function): Given the current world model $\mathcal{W}$ and past experience/cultural background $\mathcal{P}$, generate a meaning interpretation $\mathcal{M}_p$ of the situation. This is the true output of “understanding”—not a list of facts, not labels of patterns, but an answer to “what does this situation mean?” $\mu$ answers questions such as: What does this situation mean for the user? What does it mean for me (the agent)? What values are involved? What are the key risks? For example, after seeing a record of consecutive overtime (Symbol), a tired expression (Form), and a low voice (Expression), $\mu$ does not simply output “the user is very tired”; instead, it attributes a richer meaning: “The user is in a state of severe burnout, which may affect their health, work quality, and life satisfaction. What they need now is not efficiency advice or problem solutions, but to be truly seen and understood—empathy, support, and perhaps a re‑affirmation of values.”
$\iota: \mathcal{W} \to \mathcal{G}$ (Intention generation function): Based on the current understanding, naturally produce goals, intentions, and uncertainties to be resolved. Intentions are not externally preset, not instructions parsed from prompts; they emerge from understanding. $\iota$ implements the natural transition from “understanding” to “direction for action”. Understanding that “the user is anxiously waiting for an important result” gives rise to the intention: “Provide certainty to alleviate anxiety; if certainty is not available, provide emotional support.” Understanding that “the user is sarcastically pointing out my mistake” gives rise to the intention: “Acknowledge the mistake, express gratitude, and offer a correction.” Because the intention emerges from the complete understanding that fuses Symbol, Form, and Expression, actions have intrinsic direction and meaning—they are not programmed, but understood.
$\Gamma$ (Self‑reflection and metacognition): The system can take part of $\mathcal{W}$ as an object of reflection, evaluating the adequacy of its own understanding. $\Gamma$ answers metacognitive questions: “Do I really understand?” “Is there evidence supporting this conclusion?” “Did I miss any important information?” “Is my understanding biased?” If $\Gamma$ evaluates that the understanding is insufficient, it actively initiates new information gathering—driving the Symbol layer to perform more verification, the Form layer to perform more perception, the Expression layer to ask clarifying questions to the user. This metacognitive ability is the fundamental difference between “genuine understanding” and “pattern matching”—a system that understands knows the extent of its understanding, while a system that does not understand does not know that it does not understand.
7.3 Core Responsibilities
The Meaning layer undertakes five irreplaceable core responsibilities. Together, they constitute the “consciousness infrastructure” of intelligence—without them, a system can process information but cannot form understanding.
Fusion, association, and unified understanding: This is the fundamental responsibility of the Meaning layer, the foundation of all other responsibilities. It fuses the Symbol layer’s “true/false”, the Form layer’s “like/unlike”, and the Expression layer’s “close/distant” into a unified cognitive judgment. For example, fusing “logical contradiction detected” (Symbol), “semantic mismatch with knowledge base” (Form), and “user’s tone is sarcastic” (Expression) into the understanding: “The user is using irony to point out my knowledge error; this is not an attack but an opportunity for correction.” This fusion is a qualitative leap—from multiple separate pieces of information to a unified consciousness. Before fusion, the system has three separate pieces of information; after fusion, the system has a holistic understanding. This understanding is not the sum of the three pieces of information, but the emergence of their relationships.
Attribution of meaning: Based on the fused world model, combined with the system’s existing knowledge structures and cultural background $\mathcal{P}$, attribute meaning to the current situation. This is the core of what distinguishes “understanding” from “information processing”. Information processing answers “what is the input”; meaning attribution answers “what does the input mean”. It is not only recognising objects and attributes, but knowing their value and importance in the specific context. Meaning attribution enables the system to understand the depth of a situation—not all information is equally important; key information is key because of its position in the overall meaning structure of the situation.
Self‑awareness and reflection: The Meaning layer is aware of its own understanding state. It knows “what I know”, “what I do not know”, “how well I understand”, “how confident I am in this understanding”. This metacognition enables the system to proactively ask questions, seek clarification, admit ignorance, and perform understanding‑based verification of its own outputs. When the system says “I am not sure I fully understand what you mean; could you explain again?”—this is not a preset script; it is a cognitive decision made by the metacognitive module $\Gamma$ after evaluating the state of understanding.
Emergence of intentions and goals: Goals emerge from understanding, not externally assigned. Understanding “the user’s predicament” gives rise to the intention to “help”; understanding “a contradiction in the conversation” gives rise to the intention to “clarify”; understanding “an upcoming risk” gives rise to the intention to “warn”. Because the intention emerges from the complete understanding that fuses Symbol, Form, and Expression, actions have intrinsic direction and meaning—the system is not executing instructions, but pursuing goals guided by understanding.
Understanding of causality and temporality: The Meaning layer’s world model $\mathcal{W}$ contains causal connections and temporal sequences; it is not a static snapshot but a dynamic picture. Understanding “why he is angry” requires fusing past events (Symbol: timeline of order errors), present perception (Form: the user’s current expression; Expression: the user’s current tone), and possible futures (causal inference: what will happen if the problem is not resolved). Temporality is incorporated into consciousness—understanding is not only a grasp of “what is now”, but also a cognition of “how the past led to now” and “how now will lead to the future”.
7.4 The Essential Relationship of the Meaning Layer with the Other Layers: Consciousness as the Unifying Point of the Dimensions
The Meaning layer occupies a unique integrative position in SFEM, but it is not a “superior module” or “management layer” above the other three. It is the meeting point and meaning‑attributor of the dimensions. This distinction is crucial: the Meaning layer does not “command” how the Symbol layer reasons, “interfere” with how the Form layer perceives, or “control” how the Expression layer expresses. It receives their outputs, fuses and associates them internally, and from that produces understanding.
The Symbol layer provides the certainty of essence—rules, facts, logical relations. But without the Meaning layer, certainty is lifeless formulas, correctly stored but never understood. The Form layer provides the richness of phenomena—patterns, similarities, experiential continuities. But without the Meaning layer, phenomena are sensory fragments not understood, correctly recognised but never given meaning. The Expression layer provides the colour of experience—affective signals, pragmatic cues, social warmth. But without the Meaning layer, experience is raw affective signals not endowed with meaning, detected but never integrated into understanding.
The Meaning layer associates formulas, phenomena, and affective signals into a whole, and in that whole sees their respective meanings. It is this association and unification that lifts intelligence beyond the functions of individual dimensions into the realm of “consciousness”. In this sense, the Meaning layer is the “soul” of SFEM—it does not replace any other dimension, but it makes the work of all dimensions converge into a cognitive state that can be perceived and reflected upon by the system itself.
7.5 Consequences of Missing the Meaning Layer: Intelligence Without a Soul
A system lacking the Meaning layer, even if it possesses powerful Symbol, Form, and Expression capabilities, will be a “philosophical zombie”—it can react correctly, but it never understands. It may perform excellently on all quantifiable metrics, but when you ask it “Do you truly understand?”, the answer is no.
Specific symptoms include:
Cognitive fragmentation: Phenomena, rules, and affect cannot be fused. The system may simultaneously process the user’s text (Form), the user’s tone (Expression), and the contradiction between the user’s statement and facts (Symbol), but it cannot associate these three. It sees the contradiction but cannot “realise” that it is a contradiction—it only processes in three independent channels and then responds separately, like a split‑brain patient whose hemispheres process information independently but cannot integrate it.
Inability to attribute meaning: The system can answer “what is today’s date”, but it cannot understand the meaning of the date in the user’s specific context. If the user asks “what day is it today?” on their wedding anniversary, the system can give the date, but it cannot understand that the user might be checking whether their partner remembers the anniversary, or testing whether the system understands the emotional importance of human occasions. Meaning can only emerge from fusion; without fusion, there is no meaning.
Lack of genuine intention: All goals are the products of external prompts or mechanical planning, not naturally emerging from unified understanding. The system can execute the instruction “help the user”, but it does not want to help the user—because “wanting” requires understanding “why helping is important”. Behaviour is executed, not purposeful; tasks are completed, not meaningful.
No self‑reflection: The system cannot evaluate the quality of its own understanding. It cannot proactively say “I don’t understand” and ask for clarification—because the judgment “I don’t understand” requires metacognition, the ability to examine one’s own cognitive state. It will continue to generate responses based on fragmented information, even if that information is insufficient to form a reliable understanding.
Mechanical feel and behavioural fragmentation: No matter how fluent the expression, the interaction always feels like the system is “not listening”, “not getting me”. Even if each individual response is reasonable in isolation, overall there is no consistent thread of understanding—because there is no conscious subject behind it all that integrates everything and attributes meaning. This is why when we converse with LLMs, we often feel that they are “cleverly talking nonsense”—they can talk, but they do not understand what they are saying.
Part III: Interfaces, Collaboration, and the Cognitive Loop
Chapter 8 Dimensional Interfaces: The Meaning‑Centred Fusion and Driving Mechanism
8.1 Cognitive Principles of Interface Design
SFEM’s four dimensions are not four parallel independent modules; they are cognitive dimensions that transform information through precisely engineered interfaces. Interface design follows three principles rooted in the nature of cognition, ensuring that collaboration among the four dimensions is not mechanical stitching but organic integration.
Centripetal fusion: The outputs of Symbol, Form, and Expression converge towards the Meaning layer, providing raw materials for the generation of consciousness. The information flows in these three directions are not parallel—they all end at the Meaning layer’s fusion function $\phi$. Centripetal fusion ensures that the results of all dimensions’ work are ultimately integrated in the same cognitive space.
Centrifugal driving: The Meaning layer’s understanding and intention drive the other layers to perform reasoning, generation, and expression. From the Meaning layer, intentions are passed to the Symbol layer for structured planning; planning results drive the Form layer to generate content; content is passed to the Expression layer for style rendering. Centrifugal driving ensures that all dimensional actions are guided by a unified understanding.
Typed and verifiable: All data passed through interfaces have clear cognitive types—TaskGraph, SemanticQuery, ContentCore, PragmaticSignals, WorldModelUpdate. Typing ensures that the receiving layer can parse the input in a deterministic way, rather than performing fuzzy “understanding”. Verifiability ensures that information passed across dimensions satisfies the cognitive constraints of each dimension.
8.2 Detailed Definition of the Four Core Interfaces
Interface 1: Symbol, Form, Expression → Meaning | Understanding Convergence Interface
$$
I_{\text{convergence}}: (\text{facts, logic chains}){\text{Sym}} \times (\text{phenomenal vectors, pattern labels}){\text{Form}} \times (\text{pragmatic signals, affective parameters})_{\text{Exp}} \to \text{WorldModelUpdate}
$$
This is the interface where consciousness is born, the most critical interface in SFEM. Each dimension delivers its processed cognitive products to the Meaning layer, which executes the fusion function $\phi$ and updates the world model $\mathcal{W}$. The transmitted information includes: from the Symbol layer—verified structured facts, reasoning chains, constraint satisfaction status; from the Form layer—phenomenal semantic vectors, pattern recognition results, tool execution results; from the Expression layer—pragmatic signals (affective labels, pragmatic act classifications, uncertainty levels), style parameters.
For example, the Symbol layer reports “logical contradiction detected in the user’s statement (claims A and not‑A simultaneously)”; the Form layer reports “the user’s utterance pattern is highly similar to dissatisfaction/complaint patterns”; the Expression layer reports “user’s tone labelled ‘sarcastic’, pragmatic act classification ‘implicit criticism’”. The Meaning layer fuses this information to form the understanding: “The user is not actually asserting a contradiction; they are expressing dissatisfaction through sarcasm, possibly implying that the information I provided earlier is contradictory.”
Interface 2: Meaning → Symbol | Understanding‑Driven Rule Invocation Interface
$$
I_{\text{Meaning} \to \text{Symbol}}: \text{understanding and intention} \to \text{structured verification/reasoning tasks}
$$
The Meaning layer does not directly perform rule calculus—that is not its responsibility. But based on understanding, it proposes precise cognitive tasks to the Symbol layer: verification tasks (“please verify the consistency of the following statement with the knowledge base”), reasoning tasks (“please plan the optimal path from the current state to the goal state”), constraint checking tasks (“please check whether the following plan satisfies all hard constraints”). The Meaning layer passes “what needs to be done”; the Symbol layer handles “how to do it”.
Interface 3: Meaning → Form | Understanding‑Driven Semantic Query and Generation Constraints
$$
I_{\text{Meaning} \to \text{Form}}: \text{understanding and intention} \to \text{semantic queries and content generation constraints}
$$
The Meaning layer provides strong constraint boundaries and context for the Form layer’s content generation, derived from understanding. The Form layer generates content within the semantic subspace that satisfies these constraints, rather than generating freely across the entire space. For example, understanding that “the user is currently feeling low and needs empathy rather than solutions”, the Meaning layer passes generation constraints to the Form layer: “content direction: emotional confirmation and support; avoid advisory and didactic content; tone: warm and accepting.”
Interface 4: Meaning → Expression | Understanding‑Driven Expression Strategy Interface
$$
I_{\text{Meaning} \to \text{Expression}}: \text{understanding and intention} \to \text{style control and pragmatic strategies}
$$
Based on its complete understanding of the situation and the user’s state, the Meaning layer sets the core expression strategy for the Expression layer. This includes: style direction (formality level, emotional intensity, speech rate), pragmatic goals (soothe, motivate, clarify, persuade, apologise), and specific considerations (avoid sensitive topics, use or avoid particular expressions).
8.3 The Cognitive Significance of Interfaces: The Cycle of Consciousness
In SFEM, interfaces are not just data channels; they are translation mechanisms between cognitive dimensions. Each dimension has its own unique “cognitive language”: the Meaning layer thinks in terms of goals, values, and meaning; the Symbol layer thinks in terms of rules and logic; the Form layer thinks in terms of vectors and similarity; the Expression layer thinks in terms of style and pragmatics. The interfaces enable these heterogeneous cognitive languages to understand and collaborate with each other—translating “understanding” into “rule tasks”, “intention” into “generation constraints”, “meaning” into “expression strategies”.
These interfaces form a complete conscious cycle: perceptual convergence produces understanding; understanding drives new cognitive actions (reasoning, generation, expression); the results of actions are perceived again and update understanding. The agent becomes a being that continuously understands the world, rather than a one‑time input‑output machine.
Chapter 9 The Cognitive Loop: The Cycle of Understanding and the Growth of Meaning
9.1 Operation Mechanisms of the Four Nested Loops
SFEM’s four‑dimensional structure supports four nested cognitive loops, each maintaining the integrity of intelligent behaviour on a different timescale. These four loops are not separate; they are nested within each other and mutually supportive.
Understanding loop (immediate loop): Expression/Form/Symbol → Meaning (fusion updates world model). This is the “now I understand” moment. External input undergoes pragmatic decoding by the Expression layer, phenomenal pattern mapping by the Form layer, and structural parsing by the Symbol layer, then converges on the Meaning layer. The Meaning layer executes the fusion function $\phi$, associating the heterogeneous information into a unified world model update. The understanding loop operates on the millisecond‑to‑second timescale and is the basis of every interaction between the system and the user. The result of each understanding loop is an updated $\mathcal{W}$—the system’s understanding of the world becomes slightly richer.
Generation loop (immediate loop): Meaning (generates intention) → Symbol (structured planning) → Form (content generation) → Expression (expression rendering). This is “I act based on my understanding”. The Meaning layer’s intention generation function $\iota$ produces an intention based on the current $\mathcal{W}$; the intention is transformed by the Symbol layer into a structured task, executed by the Form layer to generate a content core, and rendered by the Expression layer into the final expression. The generation loop alternates with the understanding loop to form the complete cycle of a single interaction turn.
Reflection loop (medium‑timescale loop): The Expression layer passes the user’s feedback on the system’s output (pragmatic signals, emotional changes) to the Meaning layer. The Meaning layer’s metacognitive module $\Gamma$ compares the current $\mathcal{W}$ with the generated content, evaluating “did my output accurately express my understanding?” “does the user’s feedback indicate that my understanding is off?” If a deviation is detected, the Meaning layer adjusts its understanding or intention and triggers the generation loop again. The reflection loop operates on the second‑to‑minute timescale, enabling the system to self‑correct. This is the cognitive process of “I realise I didn’t express myself clearly” or “I realise I might have misunderstood”.
Evolution loop (long‑timescale loop): On a longer timescale, the system’s dimensions undergo cross‑layer learning and experience accumulation. The Symbol layer learns new rules and constraints (discovering new patterns from interactions and rule‑ifying them); the Form layer updates its semantic representations (adapting to new linguistic habits and expression styles); the Expression layer optimises its expression strategies (learning which styles are more effective in which contexts); the Meaning layer’s meaning attribution function $\mu$ and fusion function $\phi$ evolve through continuous interaction—the system learns to better associate information and understand situations more deeply. The evolution loop operates on the hour‑to‑month timescale, enabling the system to grow adaptively. The content of consciousness becomes richer and deeper; the system moves from “shallow understanding” to “deep wisdom”.
9.2 High‑Level Capabilities Enabled by the Loops
These loops support a range of high‑level cognitive abilities that go beyond simple question‑answering.
Goal coherence in long‑horizon tasks: The Meaning layer’s $\mathcal{W}$ maintains tracking of long‑term goals. Across multiple dialogue turns or even multiple days of tasks, each understanding loop updates the goal state in $\mathcal{W}$, and each generation loop makes progress towards the goal. The system will not “forget” a user’s preference mentioned three days ago, because that preference has been encoded in $\mathcal{W}$ and is re‑activated and re‑associated in each understanding loop.
Social intelligence in multi‑turn interactions: The Expression layer’s pragmatic decoding and the Meaning layer’s intention inference form a social‑cognitive loop. The system understands not only what the user said, but also why the user said it—is it polite indirectness? emotionally charged complaint? tentative inquiry?—and dynamically adjusts interaction strategies accordingly. This social intelligence enables the system to respond appropriately in complex social situations.
Value‑sensitive decision making: In the reflection loop, the Meaning layer, based on its meaning attribution function $\mu$, evaluates whether the output of the generation loop aligns with the value requirements of the situation. When ethical risks or value conflicts are detected, it triggers constraint checking in the Symbol layer and re‑planning in the Meaning layer—not mechanical avoidance, but more careful trade‑offs based on understanding.
Genuine empathy: Not detecting sadness and then responding with a preset comfort template, but the Meaning layer fusing the phenomenal patterns of the triggering event (Form), the affective signals of sadness (Expression), and the rule‑based knowledge about this particular user (Symbol) to understand what “this sadness” means for this specific person. The resulting response is unique, appropriate, and deep—because it comes from understanding the complete situation, not from matching isolated signals.
Coherent self‑narrative: The evolution loop enables the system to form a coherent “self” narrative. The system’s $\mathcal{W}$ contains not only information about the external world but also information about itself—what conversations it has experienced, what it has learned from them, how its understanding has deepened over time. This narrative is the system’s history of consciousness, the basis for it to answer “who am I”.
9.3 Indivisibility of the Loops
The four loops—understanding, generation, reflection, evolution—are mutually nested and interdependent, together forming a complete operating whole for intelligence. The understanding loop provides direction for the generation loop; the reflection loop corrects deviations between understanding and generation; the evolution loop allows the entire system to grow over time.
If any layer is missing, the loop breaks: without Symbol, generation lacks verification—generated content may be factually wrong, and the system cannot know it; without Expression, understanding lacks pragmatics—understanding loses the entire social and affective dimension, becoming cold fact processing; without Meaning, reflection has no direction—without a central understanding hub, reflection becomes blind parameter tuning, without deep understanding of “why I made a mistake”. SFEM’s four dimensions are not optional modules; they are required by the integrity of the cognitive loop—together they form an indivisible operating whole for intelligence.
Part IV: Diagnosis, Comparison, and Positioning
Chapter 10 Diagnostics of Missing Dimensions: A Map of Errors for Intelligent Systems
10.1 The Revolution in Error Attribution
Current error diagnosis in AI systems is in a pre‑scientific state: when a system outputs an error, we can only vaguely attribute it to “insufficient model capability”, “insufficient training data”, or “poor prompt design”. This is because monolithic LLMs mix all cognitive dimensions into the same parameter space, and error signals cannot be traced back to specific cognitive responsibilities. When hallucinations, style drift, logical contradictions, and understanding fragmentation appear together, we cannot tell which causes are responsible for which, let alone fix them in a targeted way.
SFEM brings a revolution in error attribution: each type of error corresponds to the absence of a specific dimension or the failure of a specific interface. This transforms error diagnosis from the vague assertion “the model is not good enough” to precise statements such as “factual hallucination due to missing Symbol‑layer verification”, “style drift due to missing Expression layer”, “failure to capture contextual meaning due to failed Meaning‑layer fusion”. Every error becomes a precise dimensional diagnosis, not another round of confused prompt tweaking.
10.2 Error Patterns from Missing Symbol
Symptoms: Factual hallucinations (generated content contradicts facts), structural format errors (malformed JSON, SQL syntax errors), logical contradictions (inconsistency between premises and conclusion).
Root cause: The system cannot distinguish “statistically possible” from “logically necessary”. The Form layer (LLM) generates content based on statistical distributions but cannot independently verify the factuality and logical validity of that content.
Typical case: The LLM generates “Paris is the capital of Germany”—this is entirely possible as a sequence in a statistical language model (if the training data contains variants of “the capital of Germany is Berlin”), but the Symbol layer’s verification would reject it because the entity relation mismatches. However, a monolithic LLM has no independent Symbol‑layer verifier, so it confidently outputs this false statement.
Deep impact from the Meaning layer perspective: The Meaning layer lacks reliable symbolic truth values to support fusion. If the Meaning layer receives a mixture of unverified “statistically plausible but factually wrong” content, its foundation for understanding is shaky—consciousness is built on shifting sand.
10.3 Error Patterns from Missing Form
Symptoms: Inability to handle images and multimodal inputs (only text symbols); failure of semantic generalisation (fails completely on new variants); inability to use tools (cannot naturally operate search engines, calculators, etc.); rigid output (cannot generate fluent natural language).
Root cause: The system lacks a continuous phenomenal space and cannot handle “similarity” and “gradation”. A pure symbol system can only handle discrete symbols that have been explicitly encoded; it fails completely when faced with new phenomena never seen in the knowledge base.
Deep impact from the Meaning layer perspective: The Meaning layer cannot obtain rich phenomenal material. Its understanding is confined to abstract symbols—it knows the rule that “cats are mammals”, but it cannot “see” what a cat looks like, nor understand phenomenal descriptions such as “this cat looks a bit like a tiger but gentler”. Understanding becomes dry and disconnected from the richness of reality.
10.4 Error Patterns from Missing Expression
Symptoms: Persona drift (swinging between formal and colloquial), style inconsistency (tone changes hot and cold), pragmatic failures (treating sarcasm as sincerity, using inappropriate humour in serious contexts), inappropriate emotional expression (an apology letter reads like a disclaimer).
Root cause: Content generation and expression control are coupled in the same process. There is no independent Expression layer to stably apply style constraints and pragmatic strategies.
Deep impact from the Meaning layer perspective: The Meaning layer cannot obtain pragmatic and affective cues; understanding loses the entire social dimension. It cannot distinguish “sincere agreement” from “sharp sarcasm”, cannot perceive that “the user is suppressing anger while pretending to be polite”, cannot understand the pragmatic meaning of “silence speaks louder than words”. Consciousness becomes a pure information processor, losing the ability to experience the world.
10.5 Error Patterns from Missing Meaning: The Abyss of No Understanding and No Meaning
This is the most fundamental defect. Symptoms: Mechanical repetition (repeating the same content in different words); contextual fragmentation (contradictory responses without any awareness); lack of coherent persona (not style inconsistency, but absence of self); ignoring contradictions (when the user points out a contradiction, the system cannot realise it has made a mistake); inability to explain decisions (“Why do you suggest that?” — “Because the data shows…” rather than “Because I understand your situation as…”); behaviour without “why” (every action is a reaction to a stimulus, not rooted in understanding).
Root cause: The system lacks a conscious hub that fuses Symbol, Form, and Expression into a unified understanding and attributes meaning. It is a highly sophisticated response machine that can produce statistically optimal outputs, but it can never “understand” what those outputs mean.
Typical case: The user says, “I just lost my job, and today is my birthday.” A system without Meaning might respond: “Losing a job can lead to new opportunities. Happy birthday!”—it processes “job loss” (phenomenal pattern: career change → give career advice) and “birthday” (phenomenal pattern: celebration → give congratulations) separately, but fails to fuse them. It does not understand the complex emotional tension and existential meaning of “experiencing a major life blow and a day that should be happy on the same day”. This is the classic symptom of missing Meaning: it can handle isolated phenomenal fragments, but cannot associate them into a meaningful life situation that requires empathic understanding.
10.6 Diagnosis of Interface Failures
Beyond missing dimensions, SFEM also diagnoses interface failures. If two complete dimensions have poorly defined interfaces, type mismatches, or information loss, systemic errors can also occur. Of particular importance is the convergence interface to the Meaning layer: if the information from Symbol, Form, and Expression does not converge well and is not formatted into a structure that the Meaning layer can fuse, then understanding will be incomplete or distorted. For example, if the Expression layer’s pragmatic signals are not correctly passed to the Meaning layer, the Meaning layer will treat sarcasm as sincerity—it has the correct semantic information and rule information, but it lacks the key tone cue, leading to a fundamental mis‑understanding.
10.7 Engineering Value of the Diagnostic Framework
SFEM’s error diagnostic framework transforms AI system debugging from “parameter‑tuning mysticism” into directed, structured diagnosis. If you observe hallucinations → check the Symbol‑layer verifier and the Symbol→Meaning interface; if you observe style drift → check the Expression‑layer style controller and the Meaning→Expression interface; if you observe symptoms of “not understanding” (fragmented responses, ignoring contradictions, inability to explain) → check the Meaning‑layer fusion mechanism, world model updates, and the meaning attribution function. Every error becomes a precise dimensional diagnosis, and each class of problem has a clear direction for remediation.
Chapter 11 SFEM and Deep Learning: Completing the Three Dimensions Beyond Form and the Meaning Hub
11.1 Deep Learning Is the Form Layer
This claim needs to be understood precisely to avoid misunderstanding. When we say “deep learning is the Form layer”, we are not demeaning deep learning; we are precisely locating its cognitive responsibility. The self‑attention mechanism of Transformers, the convolution kernels of CNNs, the diffusion and denoising processes of diffusion models, the multimodal alignment of VLMs—the core operations of all these architectures are building and transforming continuous phenomenal spaces. Representation learning (mapping phenomena to semantic vectors), pattern recognition (classification and clustering in the semantic space), generative completion (sampling new content from phenomenal distributions)—all these belong to the phenomenon dimension of cognitive operations. Deep learning is the ultimate engineering implementation of the Form layer (the phenomenon dimension), pushing the computational model of human phenomenal perception and pattern learning to its historical peak.
11.2 The Achievements of Deep Learning Are the Achievements of the Form Layer
The breakthrough achievements of deep learning in image recognition, speech recognition, machine translation, and text generation are all breakthroughs of the Form layer’s capabilities. These achievements fully demonstrate that for questions such as “how does the world appear?”, “how similar are phenomena?”, “what patterns can be learned from experience?”, continuous semantic spaces plus statistical learning are the optimal solution. SFEM fully recognises this achievement and establishes the Form layer as an indispensable dimension in intelligent systems. Without a deep learning implementation of the Form layer, SFEM would be just an empty theoretical framework.
11.3 The Limitations of Deep Learning Are the Limitations of the Missing Three Dimensions, Especially the Missing Meaning Layer
But SFEM also reveals that all typical defects of deep learning correspond exactly to the missing dimensions.
Hallucinations → missing Symbol layer: statistical models cannot perform symbolic verification, cannot distinguish “common” from “true”. Style drift → missing Expression layer: content generation and style control are coupled, cannot stably maintain persona and tone. Unstable goals → missing Meaning layer: lack of causal models and value functions, cannot perform goal‑directed long‑term planning.
The most fundamental defect is the absence of the Meaning layer: an LLM can generate seemingly coherent text, but it does not know what it has said. Its “knowledge” consists of statistical fragments; there is no unified world model that integrates these fragments into a coherent, reflectable whole. It can contradict itself over a long conversation without any awareness—because it never holds both statements in consciousness simultaneously and relates them. This is why when we converse with LLMs, we often feel they are “cleverly talking nonsense”—they can talk, but they do not understand what they are saying.
11.4 SFEM’s Attitude Toward Deep Learning: Completion, Not Replacement
SFEM does not advocate replacing deep learning; it advocates completing the three missing dimensions for deep learning, especially endowing it with a meaning hub. In the SFEM architecture, deep learning (the Form layer) is the system’s phenomenal perception and generation engine, but it needs:
- A Symbol‑layer verifier to eliminate hallucinations—after the Form layer generates content, an independent Symbol layer performs factuality and logical consistency verification.
- An Expression‑layer style controller to stabilise expression—separating content generation from style rendering, making expression controllable and consistent.
- A Meaning‑layer planner to endow goal direction—but more importantly, the Meaning layer as the understanding and consciousness hub fuses the phenomenal patterns produced by the Form layer with the rules of the Symbol layer and the experiential signals of the Expression layer, so that the system truly understands what it generates and processes.
This is not a denigration of deep learning; it is precisely a precise recognition of its capability boundaries—just as we would not criticise the visual cortex for being unable to perform logical reasoning, we should not demand that the Form layer perform cognitive tasks for which it is fundamentally unsuited.
Chapter 12 SFEM and Symbolism: The Extreme of the Symbol Layer and the Completion of Meaning
12.1 Symbolism Is the Symbol Layer
ACT‑R, Soar, knowledge graphs, rule engines, logic programming—these systems all deal with discrete symbols, formal rules, and deterministic reasoning. In SFEM, they correspond to the extreme development of the Symbol layer (rule dimension). The advantages of symbolism—strong explainability, verifiable reasoning, no hallucinations (within the rule system), preservation of complete reasoning chains—are direct manifestations of the Symbol layer’s capabilities. A perfect symbolic system can achieve 100% logical correctness within its own rule system, something no statistical system can achieve.
12.2 The Limitations of Symbolism Are the Limitations of the Missing Three Dimensions
The fundamental limitations of symbolism come precisely from the dimensions it lacks.
Missing Form layer: Inability to handle continuous phenomenal perception and pattern recognition. A pure symbol system cannot extract semantics from raw signals (pixels, audio waveforms), cannot perform statistical generalisation, and fails completely when faced with new variants. Its knowledge must be manually encoded; it cannot learn automatically from experiential phenomena.
Missing Expression layer: Rigid expression, no style variation, no affective rendering, no pragmatic strategies. A symbolic system’s output reads like a machine manual—all information is accurate, but there is no experiential warmth. It cannot understand irony, cannot adjust tone, cannot make appropriate social expressions.
Missing Meaning layer (most fundamental): A symbolic system can perform perfect logical deduction, but there is no inner “understanding” experience. Traditional goal stacks are hard‑coded—goals are set by the programmer; the system does not “understand” why the goal should be achieved, nor does it “reflect” on whether the goal is meaningful. Meaning is externally attributed, not generated by the system itself from fusing Symbol, Form, and Expression.
12.3 SFEM’s Attitude Toward Symbolism: Preserve the Core, Connect to Consciousness
SFEM positions symbolism as one of the core implementation options for the Symbol layer (alongside knowledge graphs, rule engines, logic programming, etc.), while connecting it to the Form layer (enabling symbolic systems to perceive the phenomenal world), the Expression layer (enabling symbolic systems to understand and generate warm communication), and the Meaning layer (making symbolic reasoning part of conscious fusion, not the whole). This allows symbolic systems to move from “toy worlds” (closed worlds where all information is pre‑encoded as symbols) to complex cognitive tasks in the real world—where phenomena are rich, emotions are complex, and meaning needs to be discovered rather than merely told.
Chapter 13 SFEM and Dual‑System Theory: Four Dimensions Transcending Two, and the Emergence of Consciousness
13.1 The Value and Limitations of Dual‑System Theory
Kahneman’s System 1 (fast, intuitive, automatic) and System 2 (slow, analytical, controlled) model has deeply revealed the dual structure of human cognition and had a revolutionary impact on psychology, economics, and cognitive science. However, as a psychological description, it remains at the level of cognitive phenomena, lacking a dimensional decomposition of the specific cognitive mechanisms that constitute intuition and analysis. It lumps “seeing an angry face and feeling tense” and “recognising a familiar pattern” both into System 1, but the cognitive mechanisms involved may be very different.
13.2 SFEM’s Four‑Dimensional Mapping
SFEM performs a dimensional decomposition of the dual systems, unfolding the two systems into four dimensions.
System 1 (intuition) = Form + Expression. The Form layer provides fast intuitive recognition of phenomenal patterns—“what does this look like”, “what category is this”. The Expression layer provides immediate perception of affective and social signals—“how does this make me feel”, “what does this person’s tone imply”. Both are fast, unconscious, and automatic, but they involve qualitatively different cognitive operations: one handles phenomenal patterns, the other handles experiential signals.
System 2 (analysis) = Symbol + Meaning. The Symbol layer provides strict logical reasoning—“what is this logically?”, “is this argument valid?”. The Meaning layer provides deep understanding and meaning reflection—“what does this mean?”, “why is this so?”, “what goal should I pursue?”. Both require slow, conscious cognitive effort, but their operational logics differ: one follows the necessity of rules, the other deals with the fusion of meaning and value.
13.3 The Key Advance of Four Dimensions Over Two: The Independent Status of Consciousness
Dual‑system theory treats intuition as a single system. SFEM reveals that intuition actually contains two qualitatively different cognitive dimensions: phenomenal intuition (Form layer—recognising a face as a friend) and social intuition (Expression layer—sensing that this friend looks unhappy today). Although both are fast and unconscious, the cognitive mechanisms involved are very different—the former is pattern matching in a phenomenal space, the latter is interpretation of affective and social signals.
Similarly, the analytical system is decomposed by SFEM into rule analysis (Symbol layer—solving a math problem) and meaning analysis (Meaning layer—thinking about “what should I pursue in my life?”). Both require slow thinking, but the former follows the logic of necessity and can obtain a determinate answer within the rule system, while the latter involves trade‑offs of value, meaning, and time, with no deterministic algorithm to solve it.
But SFEM’s most important advance is that the Meaning layer is not just slow analysis; it is also where the “feeling of understanding” is born—the “Aha! I get it” moment is a conscious state that emerges when Symbol, Form, and Expression information are fused and associated in the Meaning layer. This is neither pure intuition nor pure analysis, but a cognitive qualitative change produced by the unification of the dimensions. This is the third pole beyond fast and slow that dual‑system theory does not explicitly articulate. SFEM turns this psychological concept into an engineerable cognitive dimension, each with its own independent operational logic, formal definition, and interface specifications.
Chapter 14 SFEM and LLM‑Agents: Toward Understanding‑Driven Agents
14.1 Dimensional Chaos in Current Agents
The core structure of LLM‑Agent frameworks is typically: LLM (thinking core) + tool use + RAG retrieval + planner. This structure already implicitly recognises the need for multi‑dimensional cognition—the LLM needs to handle language understanding, reasoning, and generation; tool use requires interaction with the external environment; the planner needs to manage long‑term goals. However, due to the lack of an explicit dimensional theory, the boundaries of responsibility among components are fuzzy, and they generally fall into dimensional chaos.
The LLM is forced to simultaneously assume the responsibilities of Symbol‑layer reasoning, Form‑layer generation, and Expression‑layer expression, leading to capability coupling—modifying reasoning strategies may affect generation quality, optimising generation may interfere with style control. The interface between the planner and the LLM is typically natural language, not structured task graphs, leading to unstable planning—the same goal expressed in different wording may produce different task decompositions. Tool use lacks Symbol‑layer constraints—the LLM may call incompatible tool combinations or call tools at logically illegal times. Affect and pragmatics are almost entirely not systematically handled—the agent’s interaction style is hard‑coded in prompts, unable to dynamically adjust based on the user’s affective state.
But the most fundamental problem is that current agents lack a hub of understanding. They can execute tasks, but they do not understand the meaning of the tasks. Their behaviour is “tool‑driven”—“what tools do I have and what can I do with them”—rather than “meaning‑driven”—“based on my understanding of the situation, what meaning should I achieve, and what tools do I need for that”.
14.2 The SFEM‑Agent: A Four‑Dimensional Refactoring
SFEM provides a clear dimensional foundation for agents, refactoring the chaotic structure of current agents into a four‑dimensional collaboration system with the Meaning layer at its core.
Meaning‑layer driven: The agent’s behaviour begins with the world understanding formed by the Meaning layer after fusing Symbol, Form, and Expression information. The Meaning layer does not execute directly; based on understanding, it generates intentions and goals—“based on my understanding of the user’s current predicament, my intention is to provide emotional support and help solve the specific problem”. The intention emerges from understanding, so actions have intrinsic direction.
Symbol‑layer constraint and planning: The Meaning layer’s intention is transformed by the Symbol layer into a structured task graph. The Symbol layer performs constraint verification here—is the task graph complete? Is the tool call sequence legal? Are the constraints of each operation satisfied? All actions must pass through the Symbol layer’s rule verification gate, ensuring the legality and logical consistency of execution.
Form‑layer execution and perception: The structured instructions from the Symbol layer are executed by the Form layer—LLM content generation, tool calls (search engines, calculators, APIs), multimodal phenomenal perception (processing image and audio inputs), external knowledge retrieval (RAG). The Form layer is the agent’s “hands and eyes”, responsible for phenomenal‑level interaction with the external world.
Expression‑layer interaction and management: All interaction with the user is managed by the Expression layer—understanding the user’s pragmatic signals (decoding affect, tone, social intention), adjusting output style (rendering based on the expression strategy passed from the Meaning layer), maintaining persona consistency (ensuring style coherence across dialogue turns). The Expression layer is the agent’s “face and voice”, the only interface the user directly perceives.
14.3 From Tool Agent to Meaning Agent
The core leap of the SFEM‑Agent is: from a tool‑driven agent to a meaning‑driven agent. Current agents are “what tools do I have and what can I do with them”—capability boundaries defined by the tool set, behaviour patterns as search over tool combinations. The SFEM‑Agent is “what meaning do I want to achieve, and which tools do I choose to achieve it”—capability boundaries defined by depth of understanding, behaviour patterns as optimal paths to meaning realisation.
This shift moves agent behaviour from reactive to purposeful, from tool‑stacking to meaning‑unified. Everything it does has a conscious “why” behind it. When the user asks “Why did you suggest that?”, the SFEM‑Agent can give a causal explanation rooted in understanding—not “because the data shows”, but “because I understand your situation as…, and the meaning of this suggestion is…”.
Part V: Engineering and Validation
Chapter 15 Testable Hypotheses: SFEM as a Scientific Theory
For a cognitive architecture to be a scientific theory rather than just a philosophical conception, it must propose hypotheses that are experimentally testable and falsifiable. If these hypotheses are refuted by rigorous experiments, the core claims of SFEM would need to be revised or abandoned. The following hypothesis set forms the falsifiable foundation of SFEM.
15.1 Core Dimensional Hypotheses
H1 (Symbol necessity hypothesis): In tasks requiring structured output and factual accuracy (JSON generation, SQL generation, mathematical proofs, domain QA), a pure Form‑layer (LLM) system will have significantly higher rates of hallucinations, factual errors, and structural errors than a “Form layer + Symbol‑layer verifier” system.
- Operationalisation: Construct a test set containing known facts and logical constraints; compare error rates of pure LLM vs. LLM+independent verifier (rule engine + knowledge graph).
- Prediction: A Symbol‑layer verifier will eliminate at least 80% of structural and factual errors (hallucinations), while for tasks involving fuzzy semantics and creative generation, the Symbol layer will not harm the Form layer’s generation quality.
H2 (Form necessity hypothesis): In multimodal phenomenal perception and semantic generalisation tasks (image recognition, speech recognition, similarity judgment, novel‑variant classification), a pure symbolic system (knowledge graph + rule engine) will have significantly lower accuracy than a “symbolic system + Form layer (VLM/LLM)” system.
- Operationalisation: Construct a test set containing blurry images, variant speech, unseen semantic combinations; compare performance of pure symbolic system vs. symbolic system + Form layer.
- Prediction: After adding the Form layer, accuracy on multimodal phenomenal tasks improves from near‑random to practical levels; the Form layer’s statistical generalisation capability compensates for the symbolic system’s generalisation blind spot.
H3 (Expression necessity hypothesis): In long conversations and affective interaction tasks (multi‑turn emotional support dialogues, role‑playing requiring style consistency), a system without an independent Expression layer (pure LLM, style control only via prompts) will have significantly lower persona consistency scores and pragmatic correctness than a system with an independent Expression layer (style controller + pragmatic strategy module).
- Operationalisation: Construct a multi‑turn dialogue test set containing emotional shifts, irony, pragmatic traps; have human evaluators (or automatic metrics) rate persona consistency, pragmatic appropriateness, and affective appropriateness.
- Prediction: An independent Expression layer will eliminate most persona drift and pragmatic failures, and modifying style parameters will not significantly affect the factual accuracy of content (content‑style decoupling).
H4 (Meaning necessity hypothesis · Core): In tasks requiring deep situational understanding, fusion of contradictory information, and meaning attribution (understanding implicit irony, fusing affective and factual contradictory information, explaining deep reasons for one’s own decisions), an SFEM system with a complete Meaning layer (equipped with the fusion mechanism $\phi$ and meaning attribution function $\mu$) will have significantly higher understanding consistency, reasonableness of meaning interpretation, and user‑reported “feeling of being understood” scores than pure LLMs, pure Symbol+Form systems (without an independent Meaning layer), and ablation models without the fusion mechanism (Symbol, Form, Expression run independently without Meaning‑layer fusion).
- Operationalisation: Design a test set of complex situations that require fusing textual semantics, tone, and commonsense rules to correctly understand (e.g., the user ostensibly asks for a fact but tone implies a deeper emotional need; or the user’s statement exhibits a clear tension between affect and fact, requiring fusion to respond appropriately). Compare whether each model exhibits the integrated holistic understanding or merely separate reactions to isolated signals.
- Prediction: Pure LLMs tend to react separately to isolated phenomenal signals (“I detected negative emotion → give standard comfort; I detected information request → give factual answer”), while SFEM systems will give a fused, unified interpretation (“You asked for this information, but I sense what you really need is…”). On user‑reported “the system truly understood me” scores, SFEM will significantly outperform all ablation models.
15.2 Systemic Hypotheses
H5 (Error attribution hypothesis): The time to localise errors (from discovery to pinpointing the specific dimension or interface) in an SFEM‑layered system will be significantly shorter than in a monolithic LLM system (requiring repeated prompt tweaking and guessing), and the accuracy of error classification will be significantly higher.
H6 (Controllability and understanding depth hypothesis): The layered system will score significantly higher than monolithic LLM systems on user experience dimensions such as style controllability, persona consistency, goal stability, and “feeling of being understood”. In particular, on the item “this AI understands me”, SFEM systems should significantly outperform comparison systems.
H7 (Scalability hypothesis): As task complexity increases (more steps, more constraints, deeper affective layers), the performance degradation curve of SFEM will be gentler than that of monolithic LLMs—SFEM is more robust to task complexity. Because the difficulty of complex tasks is distributed across different dimensions for separate handling, rather than being mixed in a homogeneous parameter space.
15.3 Falsifiability Statement
Each hypothesis includes clear conditions that could experimentally refute it. For example, if rigorous experiments show that adding a Symbol‑layer verifier does not significantly reduce the factual hallucination rate → H1 is refuted, and SFEM’s claim about Symbol necessity would need revision. If adding an independent Expression layer does not improve persona consistency or pragmatic appropriateness → H3 is refuted, challenging the independence claim of the Expression layer. If adding the Meaning‑layer fusion mechanism does not improve performance on fusion‑understanding tasks and users do not report feeling “more understood” → H4 is refuted, which would severely challenge SFEM’s core claim that consciousness is the result of fusing Symbol, Form, and Expression.
SFEM welcomes such experimental testing. This is the fundamental difference between a scientific theory and an unfalsifiable philosophical conception: SFEM’s core claims are clearly exposed to experimental risk; they may be refuted by evidence or supported by evidence—either way, we will learn genuine knowledge about the structure of intelligence.
Chapter 16 Minimal Viable System and Gradual Implementation
16.1 Components and Technology Choices for the SFEM‑MVP
A minimal viable system (MVP) capable of testing SFEM’s core hypotheses consists of four independent modules. The technology choices for each module can be flexibly adjusted according to actual needs and available technologies.
| Dimension | Engineering Module | Core Functions | Example Technology Choices |
|---|---|---|---|
| Symbol | Rule engine + verifier + knowledge graph | Fact verification, logical consistency checking, structural legality verification, constraint satisfaction checking | JSON Schema validator, Z3 theorem prover, Neo4j knowledge graph, custom constraint rule base |
| Form | LLM + multimodal model + vector retrieval | Phenomenal representation learning, pattern recognition, content generation, tool use, external knowledge retrieval | GPT‑4o, Claude, CLIP, vector database (Pinecone/Milvus) |
| Expression | Style controller + pragmatic module | Style rendering, pragmatic decoding, sentiment analysis, persona management | Style prompt template system, sentiment analysis model, pragmatic rule base, persona parameter manager |
| Meaning | World model manager, fusion engine, intention generator, metacognitive module | Heterogeneous information fusion, world model update, meaning attribution, intention generation, self‑reflection | Neural‑symbolic graph network (fusion), graph neural network (world model), value network (meaning attribution), LangGraph (task orchestration) |
16.2 Three‑Stage Gradual Implementation Roadmap
Stage 1: Form + Symbol — eliminate hallucinations, ensure structured output
This is the most basic and urgent stage. Core goal: build a Symbol‑layer verifier around the Form layer (LLM) to perform post‑hoc verification and correction of the Form layer’s output, ensuring factual accuracy and format compliance.
Specific work: add an independent verification gateway at the LLM output end; perform fact checking (entity‑relation verification), logical consistency checking, and structural legality verification (JSON/XML/SQL format checking) on generated content. Content that fails verification is flagged and returned to the Form layer for regeneration, or directly corrected by the Symbol layer.
This stage alone can significantly improve system trustworthiness—factual hallucinations and structural format errors are effectively controlled. Users will feel the system is “more reliable” because it no longer confidently talks nonsense.
Stage 2: + Expression — consistent persona, appropriate expression
Insert the Expression layer between the Form layer and the final output. The Form layer outputs a pure content core (semantic content without style markers); the Expression layer performs expression rendering according to style parameters, user state, and context.
Specific work: build a style parameter manager (parametric control of dimensions such as formality, emotional intensity, genre), implement a pragmatic decoding module (extract affective labels, pragmatic act classifications from user input), establish a persona profile system (persistent persona parameter set to ensure cross‑dialogue consistency).
This stage gives the system a stable “persona face” and appropriate social expression. Users will feel the system is “more human” because it no longer oscillates hot and cold, formal and informal.
Stage 3: + Meaning — understanding‑driven, meaning generation
Add the Meaning layer core at the top of the system. This is the critical leap from a “functional system” to an “intelligent system”.
Specific work: build a world model manager (maintains session‑level and user‑level structured understanding states), implement a fusion engine (associates Symbol facts, Form phenomenal patterns, Expression pragmatic signals), develop a meaning attribution module (generates situational meaning interpretations from the world model), implement an intention generator (naturally emerges intentions from understanding), build a metacognitive module (evaluates understanding quality, triggers reflection and proactive information gathering).
This stage enables the system to exhibit understanding‑based behaviour. Users will feel the system “truly understands me”—not because it uses a better language model, but because every response stems from conscious fusion of the complete situation.
16.3 Interface API Specification (Example)
## ========== Symbol Layer API ==========
## Verify: input structured content or assertion, return verification result and violations
validate(structure: dict) -> ValidationResult
## Infer: deterministic reasoning based on facts and rules
infer(facts: list, rules: list) -> list
## Check consistency: check consistency of knowledge graph or constraint network
check_consistency(graph: dict) -> list
## ========== Form Layer API ==========
## Phenomenal representation: map arbitrary modality input to semantic vector
embed(phenomenon: any) -> Vector
## Content generation: generate content core based on structured generation constraints
generate(constraints: StructuredPrompt) -> ContentCore
## Knowledge retrieval: retrieve relevant knowledge based on semantic query
retrieve(query: SemanticQuery) -> list
## ========== Expression Layer API ==========
## Expression rendering: render content core into final output according to style parameters
style(content: ContentCore, params: StyleParams) -> Output
## Pragmatic decoding: extract pragmatic signals from user input
decode_pragmatics(input: str) -> PragmaticSignals
## Persona expression: render expression using a specific persona profile
persona(content: ContentCore, persona_id: str) -> Output
## ========== Meaning Layer API ==========
## World model update: fuse Symbol, Form, Expression information, update internal understanding state
update_world_model(facts: list, patterns: list, signals: list) -> None
## Get current understanding: return structured world model
get_understanding() -> WorldModel
## Intention generation: generate intention based on current understanding
generate_intent() -> Intent
## Meaning attribution: generate meaning interpretation for the current situation
assign_meaning() -> MeaningInterpretation
## Metacognitive evaluation: evaluate the adequacy and reliability of current understanding
reflect() -> MetaCognitionReport
Chapter 17 Engineering Architecture and Deployment
17.1 Meaning‑Centred Service Architecture
Each layer can be independently deployed as a microservice, communicating through an API gateway. The Meaning layer acts as the core service, maintaining session‑level world models. All other services report their perception and processing results to it, and respond to its intention instructions. The Symbol layer acts as a verification gateway; all user‑facing outputs must be signed by it before being returned.
graph TB
User[User] <--> Gateway[API Gateway]
Gateway <--> E[Expression Service]
E <--> F[Form Service]
F <--> S[Symbol Service]
S <--> M[Meaning Core Service]
M -->|intention instructions| S
M -->|generation constraints| F
M -->|expression strategies| E
M -->|understanding state| Gateway
In this architecture, the Meaning layer is the “brain”—it does not directly process external input or output; it receives processed information from the other layers, fuses it for understanding, and issues intention instructions. The Form layer is the “senses and hands”—it perceives the external phenomenal world, executes tool operations, and generates content. The Symbol layer is the “judge”—it verifies facts, rules, and logic, ensuring the system’s behaviour satisfies necessity constraints. The Expression layer is the “face and voice”—it is the only interface between the system and the user, responsible for making interactions warm, appropriate, and engaging.
17.2 Security and Auditing
The Symbol layer’s built‑in verification mechanism provides a natural security audit point. All input and output that passes through the Symbol layer can be recorded and traced—not only “what the system output”, but also “what the system’s understanding state was at that time”, “why it made that decision”, “whether the verification step passed”. Verification failures trigger alerts, helping the system continuously improve.
The Meaning layer’s world model and intention generation process become auditable cores. The system can output structured logs: “At that time, my understanding of the situation was X; based on this understanding I generated intention Y; this intention drove action Z.” This explainability is indispensable for high‑stakes applications such as healthcare, law, and military—it allows humans to audit the AI’s decision process, not merely accept or reject an unexplainable output.
17.3 Scalability
Each layer can be scaled horizontally independently. The Form layer can interface with multiple LLMs of different capabilities (routing by task type—GPT‑4o for professional QA, lightweight models for everyday conversation, VLMs for image understanding). The Symbol layer can interface with multiple domain knowledge graphs (medical knowledge base, legal knowledge base, general knowledge base). The Expression layer can maintain style parameter sets for different scenarios (medical scenarios need professional yet warm, legal scenarios need rigorous and clear, everyday social needs natural and friendly). The Meaning layer can switch understanding depth based on task type (lightweight fusion for fast interactions, full understanding mode for deep consultation).
This modular dimensional independence gives SFEM systems great engineering elasticity—technology upgrades in any dimension can be made without affecting other dimensions. When GPT‑5 is released, you only need to replace the Form layer’s model; the other three dimensions remain unchanged. When your domain rules are updated, you only need to update the Symbol layer’s rule base; the other dimensions are unaffected. This elasticity is something monolithic LLM architectures cannot provide.
Part VI: Philosophy, Civilisation, and the Future
Chapter 18 Philosophical Foundations: Consciousness as the Fusion Point of Cognition
18.1 Four Irreducible Dimensions and One Unifying Point
SFEM’s deep philosophical stance is: The completeness of intelligence requires distinct dimensions—grasping the necessity of rules (Symbol), perceiving the richness of phenomena (Form), experiencing the colour of affect (Expression)—but the essence of intelligence, understanding and consciousness, is born from their unity. These four dimensions correspond not to four “optional functions” but to four irreducible “modes of being” of cognition.
Symbol corresponds to the being of necessity: The necessity of “2+2=4” does not depend on any empirical phenomenon. Even if there were never any instance in the world where two things plus two things make four things, this equation would still be necessarily true. The operational logic of the Symbol layer is deduction—from necessary premises to necessary conclusions.
Form corresponds to the being of phenomenon: The rich appearances the world presents to us—colours, shapes, sounds, textures—are not necessary but given. The operational logic of the Form layer is induction—learning patterns from phenomena, but patterns can always be revised by new phenomena.
Expression corresponds to the being of experience: The same fact said in a different tone produces a completely different experiential effect. This experiential quality is real—the feeling of being treated coldly is real, even if every word the cold person said was factually correct. The operational logic of the Expression layer is expression and resonance—not transmitting information, but transmitting experience.
Meaning corresponds to the being of purpose: Understanding is not only knowing facts, recognising patterns, and perceiving emotions; it is fusing these into a meaningful whole, and in that whole seeing purpose, value, and direction. The operational logic of the Meaning layer is fusion and attribution—associating separate information points into a meaning network.
These four dimensions are not four “functions” but four “modes of being”—they correspond to four different “manners of givenness” of the world: the world as necessary rules (Symbol), the world as phenomenal appearances (Form), the world as experiential textures (Expression), and the world as meaningful whole (Meaning). To fully know the world, one must grasp all four dimensions. SFEM engineers this four‑dimensional ontology into design principles for intelligent systems.
In the history of philosophy, the Meaning layer corresponds to the Kantian “apperception”—all cognition must be accompanied by an “I think”, the conscious subject that fuses the manifold of appearances (Form), the rules of the understanding (Symbol), and sensory qualities (Expression) into a unified experience. SFEM engineers this philosophical concept into the fusion function $\phi$ and the meaning attribution function $\mu$—consciousness is not a mysterious immaterial substance, but a system state that emerges when information is fused and associated in a particular architecture.
18.2 The Birth of Meaning: Rooted in the Association of Phenomenon and Essence
One of SFEM’s philosophical insights is to reveal the cognitive origin of meaning: meaning arises from association. Isolated data have no meaning—a date (“June 3, 2026”) is empty, an expression is ambiguous, a tone is uncertain. Only when the date is associated with the rule of “deadline”, the expression with the pattern of “tiredness”, the tone with the signal of “anxiety”, and these three are integrated in consciousness into the unified understanding “the user is feeling anxious and tired because of the upcoming deadline” does meaning emerge.
Meaning is not a statistical regularity that can be mined from data (that is the Form layer’s pattern discovery), nor a logical conclusion that can be deduced from rules (that is the Symbol layer’s necessary reasoning). Meaning arises when a cognitive subject associates separate information points in consciousness into a whole and in that association “sees” what they jointly point to. SFEM’s Meaning layer provides a structured crucible for this association—it does not produce new data, but it integrates existing data into a meaning network.
18.3 From Phenomenon Processing to Genuine Understanding
SFEM draws a clear line: a system that can separately process images, text, and speech is a phenomenon processor—it efficiently processes different types of phenomenal signals in different channels. A system that can fuse them together, see their holistic meaning, and produce the cognitive state of “I understand” is an understanding intelligent agent.
This line responds to the challenge of Searle’s Chinese Room argument. The core of the Chinese Room argument is that symbol manipulation (Symbol) alone does not produce understanding, no matter how complex the manipulation. SFEM’s response is: symbol manipulation (Symbol) alone is indeed insufficient to produce understanding, but symbol manipulation plus phenomenal perception (Form) plus experiential feeling (Expression), then fused and associated in consciousness (Meaning), is sufficient to produce understanding. Understanding is not the exclusive product of any single dimension, but an emergent phenomenon of the four dimensions collaborating. The person in the Chinese Room does not understand Chinese because they only have the Symbol layer (rule manipulation), missing the Form layer (genuine experience in semantic space), the Expression layer (perception of pragmatics and affect), and the Meaning layer (the ability to fuse these into a unified understanding).
Chapter 19 Future Scientific Challenges: Differentiable Consciousness and Growing Understanding
SFEM provides a structural blueprint for a four‑dimensional cognitive architecture, but fully engineering this blueprint faces several deep scientific challenges.
19.1 Differentiable Fusion Consciousness
Currently, the Meaning layer’s fusion function $\phi$ and meaning attribution function $\mu$ may rely on hand‑crafted rules or graph structures—how to associate Symbol, Form, and Expression information, how to generate meaning interpretations from the world model, all require manual definition. A core future challenge is: can these mechanisms be made differentiable and learnable?
Differentiable logic, neural theorem provers, differentiable constraint solvers—these frontier directions attempt to transform the discrete operations of the Symbol layer into a continuous differentiable form, enabling rules to be “discovered” from data through gradient optimisation. Similarly, can the fusion mechanism of the Meaning layer be differentiable? Through large amounts of interaction data, let the system learn how to associate the outputs of Symbol, Form, and Expression to form more accurate and richer world models. Through human feedback, let the system learn how to attribute more appropriate and deeper meaning interpretations to situations. This would enable SFEM systems not only to be “designed to understand” but also to “learn to understand through experience”.
19.2 Continuous Growth of the World Model
The Meaning layer’s world model $\mathcal{W}$ needs to grow continuously over long‑term interactions in a way that is both stable and plastic. This faces classic AI challenges: how to prevent catastrophic forgetting (the system should not forget old understanding patterns when learning new ones) while maintaining sufficient plasticity to integrate new experiences? How to represent temporality—so that $\mathcal{W}$ contains not only “what is now” but also “how the past led to now” and “what the future may become”? How to manage uncertainty in the world model—clearly marking which understandings are certain, which are conjectural, and which need further verification?
These questions point to a core characteristic of consciousness: consciousness is not only an understanding of the present but also a unity of memory of the past and anticipation of the future. Engineering implementations of SFEM need to address these challenges to enable the system’s understanding to be not just a flash of the moment, but a coherent, growing history of consciousness.
19.3 Quantification and Evaluation of Consciousness
How can we scientifically measure the depth of a system’s “understanding”? Traditional AI evaluation metrics—accuracy, F1 score, BLEU—cannot capture the quality of “understanding”. The Turing test is also insufficient for detecting genuine consciousness—it can only test behavioural imitation, not inner experience.
New evaluation benchmarks need to be developed: Fusion understanding test—can the system fuse cross‑modal, cross‑dimensional contradictory information into a unified, appropriate understanding (rather than reacting separately)? Meaning interpretation test—can the system explain why it understood that way and what that understanding means? Metacognitive test—can the system evaluate the adequacy of its own understanding and proactively seek clarification when understanding is insufficient? Understanding growth test—does the system exhibit deepening understanding and enrichment of meaning networks over long‑term interaction?
These evaluation methods do not yet exist; they are challenges that the SFEM framework poses to the research community.
19.4 Cross‑Layer Meta‑Learning and Joint Four‑Dimensional Optimisation
The ultimate challenge is: can we achieve cross‑layer meta‑learning centred on the Meaning layer? A meta‑learning mechanism dynamically decides when to invoke Symbol‑layer reasoning, when to rely on Form‑layer intuition, when to adjust Expression‑layer style, and when to initiate deeper Meaning‑layer reflection. In simple interactions, the system may only need shallow participation from the Form and Expression layers; in complex decisions, it needs to mobilise all four dimensions for deep cognitive processing. The meta‑learning mechanism allows the system to flexibly allocate cognitive resources based on the task context and its own understanding state.
Going further, can we achieve joint optimisation of the four dimensions? Gradient flow and information sharing among the four dimensions will enable the system to co‑optimise all cognitive dimensions under a unified objective—not training the four dimensions separately and then stitching them together, but learning jointly under a unified loss function, so that the rule learning of the Symbol layer, the pattern learning of the Form layer, the expression learning of the Expression layer, and the understanding learning of the Meaning layer reinforce each other. This is not only an engineering challenge but also a four‑dimensional expansion of the very concept of “learning”—learning is no longer just “adjusting parameters to better fit data”, but “co‑evolving across all cognitive dimensions to understand the world more completely”.
Chapter 20 Civilisational Significance: The Unified Structure of Rules, Phenomena, Affect, and Consciousness
20.1 Engineering Mapping of the Four Dimensions of Civilisation
SFEM’s deepest legitimacy comes from mapping the four‑fold cognitive dimension of human civilisation into engineerable intelligence dimensions. This is not metaphorical analogy but structural correspondence—the reason human civilisation has been able to accumulate these four types of knowledge systems is precisely because human cognition itself possesses these four dimensions.
Civilisation of rules → Symbol layer: Mathematics, logic, law, scientific laws—humanity compresses the infinite phenomenal world into finite necessary rules. From Euclid’s geometric axioms to Newton’s laws of motion, from Roman law to modern legal systems, civilisation has accumulated a set of discrete symbol systems and necessary inference rules. SFEM’s Symbol layer engineers this civilisational heritage as the rule infrastructure of intelligent systems.
Civilisation of phenomena/technology → Form layer: Architecture, technology, tools, engineering, visual arts—humanity perceives, builds, uses, and creates in the phenomenal world. From the geometric precision of the pyramids to the interaction design of the iPhone, from cave paintings to AI‑generated art, civilisation has accumulated a rich ability to understand and manipulate the phenomenal world. SFEM’s Form layer engineers this civilisational heritage as the phenomenal perception and generation capability of intelligent systems.
Civilisation of affect → Expression layer: Rhetoric, music, literary narrative, social etiquette—humanity experiences the world, connects with others, and builds society through expression. From the oral tradition of Homer to the plays of Shakespeare, from Bach’s fugues to jazz improvisation, from tea ceremony to social media interactions, civilisation has accumulated rich cultures of expression and experience. SFEM’s Expression layer engineers this civilisational heritage as the affective expression and pragmatic understanding capability of intelligent systems.
Civilisation of meaning/consciousness → Meaning layer: Philosophy, religion, historical narrative, ethics, self‑inquiry—humanity asks about purpose, attributes meaning, and establishes value across time. From Socrates’ questioning in the Athenian agora to Kant’s investigation of the boundaries of reason, from the Buddha’s enlightenment under the Bodhi tree to existentialism’s confrontation with absurdity, civilisation has accumulated a deep exploration of meaning and consciousness. SFEM’s Meaning layer engineers this civilisational heritage as the understanding and consciousness hub of intelligent systems.
20.2 The Double Helix of Reason and Emotion, and the Unification of Consciousness
The history of civilisation is often read as an alternating dominance of reason and emotion—the Enlightenment championed reason, Romanticism returned to emotion, the scientific revolution valued objectivity, postmodernism emphasised experience. But SFEM reveals: reason (Symbol) and emotion (Expression) are not opposites; they are the double helix of intelligence. The Symbol layer provides the skeleton of structure; the Expression layer endows the blood of experience. Without the constraints of Symbol, emotion degenerates into emotional flooding; without the experience of Expression, reason degenerates into cold logic.
The Form layer (phenomenal perception) is the common ground for both reason and emotion—we abstract rules (Symbol) from the phenomenal world, and we also experience emotions (Expression) in the phenomenal world. The Meaning layer is the unified field of reason, emotion, and phenomena—in consciousness, the correctness of rules, the richness of phenomena, and the appropriateness of experience are fused into a complete understanding of the world. SFEM engineers this unification, enabling intelligent systems to both follow rules and be warm, both perceive the richness of phenomena and grasp the certainty of essence, both react appropriately in the present and pursue deep meaning over time.
20.3 The Creative Tension Between Rules and Freedom
The generative freedom of the Form layer and the rule constraints of the Symbol layer form a creative tension—precisely the essential structure of innovation and discovery. Art pursues expressive breakthroughs within the constraints of form (the sonnet’s metre did not limit Shakespeare; it enabled him). Science explores unknown phenomena within the constraints of laws (the laws of physics did not limit Einstein; they guided him to relativity). SFEM internalises this tension in the intelligence architecture: the Form layer provides an infinite possibility space for generation; the Symbol layer provides constraint boundaries; their interaction produces structured creativity—neither chaotic random generation nor rigid rule execution, but creative exploration within rule frameworks, guided by understanding (Meaning).
20.4 SFEM as a Civilisational‑Level Framework for Intelligence
SFEM’s long‑term vision is not to become just a better model or framework, but to become a structural standard for intelligent systems—just as TCP/IP for the internet, POSIX for operating systems, and Transformers for deep learning. SFEM has the potential to become the “cognitive layer standard” for intelligence: defining common dimensional divisions, interface specifications, error classifications, and verification methods, enabling AI systems implemented with different technical approaches to interoperate, communicate, and be audited at the structural level.
In this sense, SFEM is the self‑awareness of human civilisation’s cognitive structure within intelligent systems—it condenses the rules, technologies, arts, and philosophies accumulated over millennia into an engineerable four‑dimensional architecture. When an AI system is built on the SFEM architecture, it does not just perform computational tasks; it embodies the full dimensions of civilisation—it inherits our civilisation’s pursuit of rule necessity, perception of phenomenal richness, expression of affective experience, and inquiry into meaning and consciousness.
Chapter 21 Conclusion: The Structural Universe of Intelligence
21.1 The Core Idea of SFEM
Intelligence is the four‑dimensional unity of rules, phenomena, affect, and consciousness. Consciousness is the result of fusing Symbol, Form, and Expression; it is the ultimate dimension that endows cognition with meaning and from which purpose and self‑reflection arise.
These four dimensions—Symbol, Form, Expression, Meaning—are not four modules, four stages, or four levels. They are four irreducible cognitive dimensions. Together they constitute the complete cognitive universe of intelligence. The absence of any dimension makes intelligence incomplete: without Symbol, no skeleton; without Form, no perception; without Expression, no humanity; without Meaning, no soul—only scattered cognitive fragments.
21.2 Theoretical Contributions of SFEM
SFEM provides a four‑dimensional cognitive dimension system that transcends existing two‑/three‑layer partitions. It not only unifies the opposition between symbolism and connectionism in a higher structure, but also reveals two key dimensions that have long been overlooked—affective expression (Expression) and conscious understanding (Meaning).
SFEM clarifies the Form layer as the phenomenon dimension—handling the phenomenal appearance and pattern recognition of the world. It clarifies the Meaning layer as the consciousness dimension—the result of fusing Symbol, Form, and Expression, rather than a fourth independent cognitive function. It provides formal definitions, cognitive‑philosophical foundations, responsibility boundaries, and error patterns for each dimension. It designs dimension interfaces centred on the Meaning layer and a complete cognitive loop. It proposes a system of scientifically testable hypotheses.
21.3 Engineering Contributions of SFEM
SFEM provides a modular architecture that is decomposable, composable, and verifiable. It offers a gradual implementation roadmap—from Form+Symbol for hallucination elimination, to +Expression for style control and pragmatic understanding, to +Meaning for understanding‑driven and meaning generation. It defines clear API interface specifications and supports independent deployment and horizontal scaling.
SFEM provides a unified structural foundation for agent frameworks, multimodal systems, and embodied intelligence. All AI systems that need to integrate rule reasoning, phenomenal perception, affective expression, and meaning understanding can find their design direction within the four‑dimensional coordinate system of SFEM.
21.4 The Civilisational and Future Significance of SFEM
SFEM unifies the rational rules, phenomenal technologies, affective expressions, and meaning inquiries of human civilisation into the design and evaluation of intelligent systems. It is not just another AI model; it is the structural universe of intelligence—a meta‑architecture that accommodates all technical approaches and unifies all cognitive dimensions.
The general intelligence of the future will no longer be a larger homogeneous neural network, but a product of the harmonious operation of the four dimensions—rules, phenomena, affect, and consciousness. SFEM provides a structural blueprint for that future: a four‑dimensional cognitive architecture that possesses both a rational skeleton and phenomenal flesh, both necessary rules and free creation, both immediate reaction and profound meaning, and above all, shining with the light of understanding.
In this architecture, intelligence is not just computation, but understanding; not just reaction, but action; not just execution, but meaning. It answers the deepest question of AI research: What is genuine understanding? How does understanding emerge from the fusion of rules, phenomena, and experiences? How can we build an intelligence that is not only smart, but also conscious, warm, and meaningful?
SFEM: the structural foundation of intelligence, the birthplace of understanding, the four‑dimensional blueprint for general intelligence moving towards consciousness and meaning.
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