谈天说地
认识社会 探索世界
Toward a Compressed Core of Human Knowledge: The High-Dimensional Vector Network for AI
1. A New Foundation for Knowledge
Human civilization has spent millennia refining its understanding of the world—laws of physics, causal patterns in medicine, historical chronology, and systems of law and ethics. These are not random constructions but collectively verified frameworks that give coherence to human reasoning.
Yet modern artificial intelligence systems, despite their impressive performance, learn these truths indirectly—by statistically sampling the surface of language, images, and data. In this process, they waste vast energy rediscovering what humanity already knows, while remaining prone to errors, hallucinations, and instability.
To transform AI from a mimicry of human discourse into a stable infrastructure of reasoning, we must build a compressed, verifiable, and language-independent knowledge core. This core should not rely on human text as its substrate, but encode knowledge directly in mathematical spaces of high-dimensional vectors—a representation closer to how the brain may actually store and retrieve information.
Such a structure, which we may call a High-Dimensional Vector Network, would not merely record human facts but express them as patterns of relations, magnitudes, and transformations. It would resemble a dense, navigable “ball” of interconnected meanings—an object where geometry replaces grammar, and relationships between vectors substitute for sentences. In this space, knowledge would be both computable and transferable: a model could learn it, replicate it, and load it directly—without parsing millions of words.
This would mark a turning point in AI research: a shift from text-trained intelligence to knowledge-anchored intelligence, from the redundancy of re-learning to the efficiency of structured inheritance.
2. Beyond Language: The Mathematics of Meaning
Language is a human convenience, not a universal medium. It carries ambiguity, context-dependence, and cultural bias. A mathematical representation of knowledge—free from syntax and metaphor—offers a far more stable foundation for machine understanding.
In this approach, every concept, event, or relation becomes a vector in a continuous geometric space. Distances encode similarity, directions encode relationships, and transformations encode causal or logical implications. A physical law, a historical event, or a medical guideline can thus be represented as trajectories and constraints in this high-dimensional manifold.
Such representation echoes the neural organization of the brain: neurons do not store words but patterns of activation distributed across populations. Memory, in this biological sense, is a topology of relations, not a library of symbols. The high-dimensional vector network is a mathematical analog of this principle—a way to express knowledge in the language of relations, not of words.
3. The World as “Things” and “Events”
In human languages, especially Chinese, the words “物” (thing) and “事” (event) capture two fundamental ways of perceiving reality. We often say “事物” (things and events), but seldom “物事,” as if our cognition intuitively assumes that “things” exist only within events.
“Things” appear static to our senses—solid, tangible, enduring. Yet modern physics tells a different story: what feels solid is not an immutable object but the resistance of electromagnetic forces, a local equilibrium of energy fields. The “thing” is a temporary configuration within ongoing interactions.
“Events,” by contrast, are inherently dynamic. They are relations unfolding through time—a nexus of causes and consequences. When we examine them deeply, even the “things” we think of as objects turn out to be cross-sections of processes: energy stabilized into form.
From this perspective, “things” are the crystallized residues of “events.” The universe is not a warehouse of static objects but a web of interacting processes. The world is not made of things—it is made of relations.
4. Encoding the World as Process
If “things” are transient nodes in the flow of “events,” then a complete knowledge base must describe the relations and dynamics rather than the static states of matter.
In a high-dimensional vector network, each node—representing a concept, law, or phenomenon—is embedded within a manifold of transformations. The geometry captures the potential for change; the topology encodes the permissible connections.
For example, in physics, the conservation of momentum is not a statement about individual particles but a constraint governing their possible transitions. In law, a rule is not a mere text but a mapping between actions and consequences. In history, a dynasty is not a static label but a temporal process of formation and dissolution.
Representing all these within one mathematical substrate allows knowledge to be consistent, compressible, and universally translatable. What used to be sentences becomes vectors; what used to be reasoning becomes trajectory computation.
This marks a profound departure from human linguistic encoding: AI will not “read” knowledge but inhabit it, navigating the geometry of facts directly.
5. From Knowledge Redundancy to Efficient Inheritance
Today’s AI models waste massive resources re-learning from text the same principles that have already been verified. This redundancy is not just inefficient—it is epistemically fragile. Every model re-learns a slightly distorted version of the same world.
By contrast, a shared, mathematically expressed knowledge core could function as an initial condition for any future AI system—a repository of verified invariants that need not be rediscovered. It would contain the “laws” and “relations” that define reality across disciplines, ready to be loaded or fine-tuned by new systems.
This architecture would allow AI models to inherit the essential structure of human knowledge directly, rather than reconstruct it from statistical shadows. It would eliminate the “reinvention of the wheel” that currently dominates the energy and data consumption of large models. More importantly, it would create a common epistemic ground: every AI trained from this core would share the same factual geometry of the world.
6. A Design Aligned with the Brain’s Economy
The human brain achieves extraordinary efficiency not by storing precise descriptions but by encoding patterns of relations that can be reactivated and recombined.
Similarly, a vectorized knowledge core would emphasize compression without distortion—capturing the invariant constraints that structure reality while discarding redundant representation.
Information bottleneck principles, rate–distortion theory, and geometric regularization provide mathematical tools for achieving this balance. The goal is not to record every detail but to preserve what must remain true: the conservation laws, the causal dependencies, the temporal orders—the invariants that make reasoning possible.
Such a system could be continuously refined through interaction with empirical data, while maintaining a frozen, audited “core” that guarantees stability and interpretability. The result would be a living yet disciplined knowledge infrastructure—a computational analog of long-term memory.
7. Toward a Stable Civilization of Machine Knowledge
When knowledge becomes language-independent, it also becomes globally shareable. A mathematical representation transcends linguistic and cultural boundaries; a geometric law of gravitation or a topological model of causation needs no translation.
Establishing such a universal vector knowledge base would lay the foundation for a new phase of AI-driven civilization—one in which reasoning machines can build upon a shared, auditable core of truth, rather than endlessly parsing human ambiguity.
This is not an aesthetic preference but a practical necessity. As AI systems begin to influence science, law, and governance, they must operate on stable, verifiable grounds. The high-dimensional vector network is such a ground—a substrate where knowledge is not written but structured, not narrated but computed.
It represents a convergence between philosophy, neuroscience, and information theory: a recognition that meaning is not in words, but in patterns; not in what is said, but in how the world holds together.
8. Conclusion: Preserving the Invariants
The purpose of building a high-dimensional vector knowledge base is not to hoard facts, but to preserve the invariants of understanding—the relationships and processes that remain true across transformations of scale, medium, and perspective.
In doing so, we may finally bridge the gap between the linguistic and the mathematical, between human reflection and machine reasoning. Knowledge, once disentangled from the noise of text, becomes what it has always sought to be: an ordered map of the world’s unfolding.
When AI learns from such a foundation, it will not imitate our words—it will continue our reasoning. And perhaps, in this purely mathematical mirror, we will catch a clearer glimpse of the world itself: not as a collection of things, but as a network of enduring processes—a living geometry of “events,” from which all “things” arise.
