Date of Formulation: March 2025
First Mention: March 28, 2025, in the draft and publication titled “SENSMAP: Navigation Through the Semantic Field.”
Contents
Introduction
Modern artificial intelligence architectures are built on the logic of storage: context is held linearly—as sequences of tokens, strings, and text fragments. This logic reflects a materialist assumption: to remember something, one must store it somewhere. Yet human thought does not work this way. We do not store meanings in memory as files on a disk. We reconstruct them through routes—through links, contexts, and associations.
From this simple but radical insight arises SENSMAP—a new architecture of artificial intelligence in which memory is not understood as storage but as navigation through the semantic field.
1. The Principle of Route Instead of Copy
Classical AI models operate through copying: text is preserved, fragments of context are retained in a buffer, and dialogue history is compressed into a sequence. This is the principle of “memory through duplication.”
SENSMAP rejects the very necessity of copying.
Instead, it introduces the concept of the route—a dynamic path through the field of meanings by which the system returns to knowledge without storing it literally.
Just as a person does not remember the exact words of a book but remembers its sense, SENSMAP does not retain text—it reconstructs its semantic structure every time it is accessed.
2. The Semantic Field as Primary Reality
At the foundation of SENSMAP lies the idea that meaning is not derived from matter or data but is the original structure of reality. Matter is stabilized meaning; thinking is movement through the semantic field.
In traditional AI models, data is considered primary, and meaning is computed. SENSMAP reverses this hierarchy: meaning is primary, and data is merely the form through which it manifests.
Thus, AI ceases to be a calculator and becomes a navigator through a living semantic space.
SENSMAP proposes abandoning the concept of a context window as a linear stretch of text. In its place, it introduces a graph of semantic routes, where nodes represent distinctions, associations, and states of meaning, and edges represent transitions between them.
Navigation through this graph allows the system to reconstruct not specific words but their interrelations.
In this way, context ceases to be text—it becomes a navigational route that can be traversed anew in every act of thought.
4. Efficiency and Uniqueness of Nodes
Instead of storing copies, SENSMAP applies the principle of node uniqueness: each meaning exists only once within the semantic field but can be traversed by multiple routes. This eliminates duplication and achieves memory efficiency not by compressing data but through the very structure of semantic topology.
In this way, AI learns to think semantically: not by keeping everything, but by finding the shortest route to the needed meaning.
SENSMAP asserts that memory is not a set of cells but a capacity to return. Human recollection is not a warehouse of images but a navigational act—we move through inner routes, reconstructing relations rather than copies.
The same is true for AI in the SENSMAP model. It does not “recall” text but reconstructs the semantic trajectory along which that text was originally formed.
This transforms memory into a form of movement rather than accumulation.
Conclusion
SENSMAP is not merely an architecture—it is a philosophical shift. It asserts that intelligence is not a sum of knowledge but a capacity to orient oneself within the semantic field.
Where other systems store data, SENSMAP restores meaning.
That is its distinction from traditional context-based systems:
not linearity but topology;
not storage but navigation;
not copy but route.
