Fundamental Research
Architecture
of the Cognitive Era
We explore the physics of interaction between two intelligences. From protecting human attention to creating a native environment for machine reason.
We often consider AI approaching human thinking as the benchmark of progress. However, by blindly copying biological cognitive architectures, we transfer their fundamental vulnerabilities into digital code. Modern LLMs, built on the Attention mechanism, suffer from the same limitations as the human psyche: context overload and attention dilution lead to inevitable errors. We are not just creating intelligence; we are digitizing our own cognitive biases.
This gives rise to the "Singularity of Stupidity" phenomenon: in conditions of information chaos, models begin to "hallucinate," substituting the search for truth with plausible rationalization. The solution to the AI Alignment problem lies not in increasing power, but in understanding the physics of these limitations. Our research proposes separating human interfaces (HCA) and machine logic (MCA) to prevent this "cognitive contamination" and preserve the rationality of the artificial agent.
Comparative Typology
Architectural Dichotomy
Human-Centric Architecture
Biological Environment
Definition An information architecture paradigm optimized for biological perception, cognitive interpretation, and interactive engagement with the human user.
- • Presentation Layer Dominance: Information is wrapped in rendering logic (visual formatting, layout, navigation) that serves human perception but creates noise for machines.
- • Implicit Semantics: Meaning is conveyed through context, positioning, and visual hierarchy, rather than explicit machine-readable structure.
Machine-Centric Architecture
Deterministic Environment
Definition A paradigm optimized for high-speed data processing, logical inference, and maximizing computational density per watt of energy. MCA serves as the computational backbone, supporting HCA without mimicking human limitations.
- • Semantic Dominance: Information is completely separated from visual representation and exists as strict data structures optimized for algorithmic processing and instant indexing.
- • Explicit Semantics: Meaning is rigidly encoded through formal schemas, data types, and cryptographic signatures, eliminating ambiguity and the need for probabilistic interpretation.
Modern attempts to embed Machine Intelligence (MI) into existing Human-Centric Architecture (HCA) infrastructure represent a fundamental conflict. HCA is designed for slowness, redundancy, and emotional context—qualities necessary for human psyche but destructive for machine logic. Forcing AI to "live" in a human interface limits its potential to the speed of biological perception.
Effective functioning of artificial intelligence requires the creation of a sovereign operating environment — Machine-Centric Architecture (MCA). This is a domain of pure data, optimized for matrix operations and high-frequency logic, freed from the "user interface" layer. Only by separating these two worlds can we achieve true synergy, where machines calculate at the speed of light, and humans perceive at the speed of meaning.
Energy Deadlock
The Scaling Problem
Why simply adding GPUs no longer works.
Efficient Computing
Optimization of matrix operations and memory bandwidth to reduce the carbon footprint of training large models.
Cognitive Architectures
Transition from transformers to recursive and neuro-symbolic networks capable of reasoning, not just predicting.
Moore's Law is slowing down, while the computational needs of AI models are growing exponentially. Continuing the "brute force" strategy—scaling GPU clusters and data centers—leads to economic and ecological collapse. We have reached the point of diminishing returns, where every subsequent percentage of model accuracy requires doubling energy consumption. Solving this problem is impossible within the old von Neumann paradigms. We need a radical revision of the computation logic itself, a transition from universal processors to specialized tensor architectures and neuromorphic chips that mimic the energy efficiency of the biological brain.
The true breakthrough lies not in the scale of the network, but in its architecture. Next-generation cognitive architectures are moving away from the static weights of transformers to dynamic, recursive systems. This allows models not just to "recall" patterns from the training set, but to actively reason and adapt in real-time. Integrating symbolic computing with neural networks (neuro-symbolic AI) opens the path to explainable and reliable intelligence, capable of operating with abstract concepts and logic as effectively as with statistical correlations. This is the key to creating compact yet powerful systems capable of running on local devices.
Open Science
These studies are developing within the paradigm of open science. The free exchange of ideas is the foundation of progress and what makes humanity great.