Building Reasoning Machines

Symbolica is building the new foundation for large-scale AI — controllable, interpretable, reliable, and secure.

Our Thesis

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.

 

Research Program

Structured Cognition

Next-token prediction is at the core of industry-standard LLMs, but makes a poor foundation for complex, large-scale reasoning. 

Instead, Symbolica’s cognitive architecture models the multi-scale generative processes used by human experts.

Whereas competitors devote trillions of model parameters to opaque, implicit memorization of their training corpus, Symbolica models possess explicit episodic memory. Our factorization of reasoning and recall enables new scaling curves and more reliable inference.

Symbolic Reasoning

Our models are designed from the ground up for complex formal language tasks like automated theorem proving and code synthesis.

Unlike the autoregressive industry standard, our unique inference model enables continuous interaction with validators, interpreters, and debuggers.

Reliable and Compliant

Symbolica models enable continuous control and guidance that is unavailable in existing LLMs, so that customers can deploy models with confidence in the reliability of their outputs.

Our explicit episodic memory model reduces hallucinations and enables data privacy guarantees that are impossible with the monolithic models trained by competitors.