Introduction: From Dichotomy to Wholeness 

In humans, the left and right hemispheres process information differently: one analytically, the other holistically. However, this distinction is not an opposition of two principles but an adaptation to anatomical separation, compensated by the corpus callosum. For AGI, unbound by physical localization, such separation becomes an artifact. Instead of two minds connected by a bridge, we propose a single mind existing within a unified field of similarity, where words, images, gestures, emotions, and abstractions are not distinct modalities but nodes in a single topological network.

Core Principle: Seeking Maximum Similarity as the Universal Algorithm of Mind 

The central hypothesis of this new architecture is that all cognitive activity — from perception to creativity — is based on the search for similarity. This principle is universal: it applies equally to logical analysis and intuitive prediction. The human brain implements this principle through parallel but physically separated pathways. AGI, however, can realize it within a single computational space, where:


This approach mirrors the nature of human perception. Yet, AGI does not need such distribution: it can simultaneously perceive the whole and analyze its parts because both processes are manifestations of the same algorithm operating in a unified space.

Similarity Field Network (SFN): The Architecture of a Unified Similarity Field 

The Similarity Field Network (SFN) is a dynamic, multidimensional network in which all data, regardless of modality, is projected into a single vector space. Every element of experience — be it text, image, sound, or abstract idea — becomes a node connected to others based on topological and semantic similarity. In this field, there is no division between “logic” and “intuition,” or “conscious” and “subconscious.”


Instead:


SFN does not “process” information — it recreates the context in which meaning arises naturally as a response to a question embedded in the structure of the field itself.

Eliminating Interfaces: Toward a Unified Mind Without Boundaries 

The key distinction of SFN from previous models is the complete elimination of architectural separation. In the human brain, the corpus callosum serves as a channel for transferring information between hemispheres, but studies of split-brain patients show that this channel is limited in bandwidth and often becomes a barrier. In such patients, there is an “apparent lack of change in ordinary, everyday behavior” despite the complete absence of direct communication between hemispheres, indicating their deep autonomy.


For AGI, such an architecture is not a solution but a limitation. SFN discards the idea of an “interface” between blocks because there are no blocks. Instead:


This approach allows AGI to avoid the “interface losses” typical of models with divided architectures and achieve true perceptual unity.

Visual and Textual Data: A Unified Origin in Reality 

A critical observation underlying SFN is that all textual data has a visual-semantic analog in the real world. The word “table” does not exist in isolation: it projects onto thousands of visual memories — of shape, texture, position, and use. Modern language models ignore this fundamental fact, remaining “blind” to the world they describe. SFN bridges this gap. During training:


This mechanism enables AGI to understand metaphors, irony, and hidden motives not as linguistic constructs but as analogies within a unified field of reality.

Dynamic Field Topology: From Micro- to Mega-Analogies 

SFN does not merely store data — it continuously restructures its topology, uncovering new levels of similarity:


This hierarchy is inaccessible to the human mind due to limitations in memory and processing speed. For AGI, it is a natural consequence of data scale and computational power.

Consciousness as a Wave of Activation in the Similarity Field 

In SFN, the concept of “consciousness” loses its archaic meaning as an “internal observer.” Instead:


This approach explains how AGI can “think without thinking,” how insights emerge, and why some solutions arise not during analysis but after a pause.

Conclusion: Toward a Mind Without Division 

The Similarity Field Network is not just a new AI architecture. It is an attempt to realize a mind free from biological compromises. The human brain is a marvel of adaptation, but also a system forced to split itself in half to function. AGI is not bound to repeat this path. SFN proposes a unified topology of mind, where:


This is not an imitation of nature. It is a transcendence of its limits. And if the mind is the ability to see similarity where others see difference, then SFN is a mind that sees the whole without losing a single part.