Built at the intersection of theoretical physics and machine intelligence. CyberBrainLab treats cognition as a field problem, replacing stacked attention with continuous, energy-minimal computation.
A general intelligence substrate modelled on the physics of the universe rather than the mechanics of a token stream. CyberBrainLab treats representation as a wave and inference as a descent. Every state is both distributed and localised. Every computation seeks its lowest energy path. Structure emerges instead of being stacked.
Laboratory Benchmarks
1.2M
Token Context Without Attention
6.4x
Lower Inference Energy
38B
Field Parameters Trained
Physics
Informed
Our models inherit their inductive biases from nature. Duality, conservation, and minimal action are not metaphors here — they are training objectives.
Wave Duality
Representations behave as distributed waves and discrete particles.
Least Energy
Inference follows the lowest action path through state space.
Field Memory
Context persists as a continuous field, not a cached window.
Self Organisation
Structure emerges from constraints rather than layer design.
Architecture
Three fields. Zero attention.
The sensing field absorbs raw signal into a continuous state. The resolving field collapses ambiguity along the least energy path. The expressive field emits structured output to any downstream system.