arXiv:2605.00265v2 Announce Type: replace Abstract: Real-world knowledge is often organized as hierarchies such as product taxonomies, medical ontologies, and label trees, yet learning hierarchical representations is challenging due to asymmetric structure and noisy semantics. We introduce Polaris, a polar hyperspherical embedding framework that separates semanticity from hierarchy using angular geometry and radius, enabling the learning of meaning and structure without interference. To map latent representation onto the sphere, we project it to the tangent space at the north pole, apply the e
The proliferation of complex, real-world hierarchical knowledge in AI systems necessitates more sophisticated methods for representation learning to improve model performance and generalization.
This research introduces a novel embedding framework that could significantly enhance AI's ability to understand and utilize structured knowledge, accelerating progress in various AI applications.
The separation of semanticity and hierarchy in knowledge representation allows AI models to learn more robust and interpretable hierarchical structures without previous interference issues.
- · AI researchers
- · Companies with complex data taxonomies
- · Generative AI development
- · Knowledge graph platforms
- · Companies relying on less efficient hierarchical learning methods
Improved performance of AI models in tasks requiring hierarchical understanding, such as recommendation systems or medical diagnostics.
Faster development and deployment of AI agents that can navigate and reason over complex, structured knowledge bases.
Potential for a new wave of AI applications that depend on highly accurate and scalable hierarchical concept learning, further collapsing certain white-collar workflows.
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Read at arXiv cs.LG