The Next Dimension in Structured Machine Learning
Using a powerful mathematical toolkit based on category theory, we move beyond next token prediction towards true structured reasoning.
Approach
All current state of the art large language models such as ChatGPT, Claude, and Gemini, are based on the same core architecture. As a result, they all suffer from the same limitations.
Extant models are expensive to train, complex to deploy, difficult to validate, and infamously prone to hallucination. Symbolica is redesigning how machines learn from the ground up.
We use the powerfully expressive language of category theory to develop models capable of learning algebraic structure. This enables our models to have a robust and structured model of the world; one that is explainable and verifiable.
It’s time for machines, like humans, to think symbolically.