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.

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.

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.