Modern AI curation tools rely on Euclidean geometry (flat space).
But real-world data—like biological taxonomies, social hierarchies, and medical diagnoses—is
complex and hierarchical.
When you force this complex data into a flat box, you run out of room.
To fit the "Majority," the math crushes the "Minority" and "Rare" cases together.
We call this Representation Collapse.
The Solution: Hyperbolic Space
HyperView uses the Poincaré disk, a model of hyperbolic geometry where space expands exponentially towards the edge.
This gives "infinite" room for outliers, ensuring they remain distinct and visible.
FAQ: Why does this matter?
The "Hidden Diagnosis" Problem
Imagine training an AI doctor on 10,000 chest X-rays:
• 9,000 Healthy (Majority)
• 900 Common Pneumonia (Minority)
• 100 Rare Early-Stage Tuberculosis (Rare Subgroup)
In Euclidean Space: The model runs out of room. It crushes the 100 Tuberculosis cases into the Pneumonia cluster. To the AI, they look like noise. The patient is misdiagnosed.
In HyperView: The Tuberculosis cases are pushed to the edge. They form a distinct, visible island. You can see them, select them, and ensure the AI learns to save those patients.
HyperView Interactive Demo
Drag to Pan. Experience the "infinite" space.
Notice how the red "Rare" points expand and separate as you bring them towards the center.
Majority
Minority
Rare
Hyperbolic Mode:
Space expands exponentially.
Rare items are distinct.