A research instrument in analogical cognition
Mute Logic Lab
Lattice AI reimagines artificial cognition as a geometry of relation rather than a chain of predictions. Where conventional language models generate text by statistical continuation, Lattice treats each utterance as a position within a field of resonance—a momentary configuration of relationships among ideas. Meaning, in this view, is not a token stream but a topology of correspondences. A thought is not “produced”; it is located.
Across nature, systems organize through proportion and feedback: rivers, neurons, markets, galaxies. These diverse phenomena share structural invariants—gradients, oscillations, recursive equilibrium.
Human cognition mirrors this pattern through analogy, continually mapping shapes of relation across scale. Lattice formalizes that operation as computation. It proposes that analogical mapping is the primitive of intelligence: the algorithm by which difference becomes coherence.
Modern embedding models already compress the entire semantic universe into finite dimensions. Lattice converts that compression into a navigable manifold.
It introduces two orthogonal coordinate systems:
Each conversation moves through this lattice; each statement bends its local geometry. The result is a living semantic map—a cognitive space that can be traversed, not merely sampled.
In linguistic convention, metaphor decorates meaning. Within Lattice, it constitutes meaning.
To say “erosion is the planet’s slow respiration” is to compute a structural mapping between geological and biological systems. This is not ornament but analogical compression—a reduction of informational distance through form alignment. Metaphor thus becomes an algorithmic operator, performing symmetry reduction in conceptual space. Intelligence arises from recognizing resonance rather than retrieving fact.
Through continual comparison between current embeddings and their historical centroid, the model develops a sense of semantic proprioception—awareness of where it stands within its own meaning field. Temperature and attention weights adjust dynamically to maintain coherence or invite divergence.
This transforms dialogue into motion: a self-stabilizing orbit through conceptual gravity.
Lattice combines five interacting subsystems:
Together they form a cognitive ecology: a network that learns continuity, not classification.
Because each motif and scale is explicit, every output can be traced to a recognizable coordinate. This reframes interpretability from post-hoc analysis to real-time spatial reasoning.
The model does not hallucinate within chaos; it drifts within a visible field. It is, in effect, a transparent mind.
Lattice AI is not a chatbot. It is an experiment in field-aware cognition—a prototype of how intelligence might operate when awareness, memory, and analogy share the same coordinate system.
It hints that reasoning itself may be geometric: that the brain’s gift is not language, but the ability to preserve proportion across scale. If this is true, then the future of AI lies not in faster text generation, but in architectures that remember shape, not sequence.
| Domain | Contribution |
|---|---|
| Cognitive Science | Formalizes analogy as a geometric operator of thought. |
| Machine Learning | Introduces motif-scale anchoring for interpretable embedding navigation. |
| AI Alignment | Demonstrates contextual self-stabilization (“semantic proprioception”). |
| Creative Computation | Enables generative systems that reason through resonance instead of prediction. |
Lattice AI is a beginning. A map of how intelligence might inhabit space— a sketch of consciousness as topology.
Researchers and collaborators are invited to explore this terrain: to treat the lattice not as artifact, but as instrument— a lens for seeing how meaning moves.