probabilistic-programming

We leverage probabilistic programming principles to tackle complex tasks, and utilize differentiable and classical programming paradigms with their respective strengths. The framework introduces a set of polymorphic,
compositional, and self-referential operations for multi-modal data that connects multi-step generative processes and aligns their outputs with user objectives in complex workflows. As a result, we can
transition between the capabilities of various foundation models with in-context learning capabilities
and specialized, fine-tuned models or solvers proficient in addressing specific problems. Through
these operations based on in-context learning our framework enables the creation and evaluation of
explainable computational graphs. Finally, we introduce a quality measure and its empirical score
for evaluating these computational graphs, and propose a benchmark that compares various stateof-the-art LLMs across a set of complex workflows.