symbolic-reasoning

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.

This post is about using contracts to enforce semantic correctness of a generative process. I will explain how contracts work in symbolicai, the benefits they offer, and the challenges ahead. Most interestingly, I think contracts could be a promising approach to alleviating hallucinations. There’s still a lot to refine, but the potential here is too compelling to ignore.

But here is the thing: if you showed a video generation model enough footage of people using spreadsheets, it would produce the exact same result. You type =A5+1, and a 2 appears. Not because anything was computed. Because the model knows what happens next. It has seen this scene ten thousand times. The 2 is not the output of arithmetic. It is the output of pattern recognition so thorough that it becomes indistinguishable from arithmetic. I mean, nothing really crazy if you think about it. It’s the same principle that allows ChatGPT to write you a perfectly functional Python script despite having never, in any meaningful sense, learned to program. It just knows what code looks like when it’s going well.

Despite appearances, perhaps the internal representations of neural networks implicitly realize symbolic structure. In support of this hypothesis, we show that the vector representations of a variety of neural networks can be closely approximated with symbolic structures: we can replace the network's entire representation-generating process with a closed-form equation instantiating a symbolic structure, and the network's behavior remains largely unchanged.