The Boundary Between Symbolic and Statistical Intelligence

The dominant paradigm of LLM development assumes that the growth of solution quality is linearly related to the growth of the power (and cost) of the underlying model.

The talk challenges this assumption by showing that with a well-thought-out engineering architecture — the distribution of tasks among short-lived agents, the accumulation of structured state, and strict symbolic control — comparable-quality results can be achieved at a fraction of the cost of models.