Language Models

Beyond Embeddings: Structured Knowledge in the Age of LLMs

Vector embeddings excel at similarity but struggle with relationships. We explore hybrid architectures that combine the best of neural and symbolic approaches.

Shep Bryan
Shep Bryan
Founder
Abstract geometric network visualization

The embedding revolution has transformed how we represent meaning in machines. But for all their power, embeddings have a fundamental limitation: they collapse relationships into distance.

What Embeddings Miss

Consider the relationship between 'doctor' and 'patient.' In embedding space, these concepts are close—they co-occur frequently. But the nature of their relationship—who treats whom, who depends on whom—is lost. This matters when we need to reason, not just retrieve.

Research by

Shep Bryan
Shep Bryan
Founder

Shep is the founder of Penumbra, building knowledge systems that transform how teams capture, connect, and leverage institutional intelligence for strategic decisions.

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