Discovering the Logic of the World with Transformers

I am always on the lookout for innovative ways to leverage AI to solve real-world problems.

One area that has recently caught my attention is the use of transformers for symbolic regression — finding mathematical and logical expressions that fit a dataset. In particular, a new paper titled “Boolformer: Symbolic Regression of Logic Functions with Transformers” demonstrates how transformers can be trained to perform symbolic regression of Boolean logic functions.

As someone passionate about interpretable and trustworthy AI, I believe this technique holds great promise for advancing AI’s understanding of logical reasoning.

Why Symbolic Regression Matters for Interpretability

Many modern AI systems rely on complex neural networks that learn implicit statistical relationships from data.

While powerful, these black-box models lack interpretability — we cannot understand the reasoning behind their predictions.In contrast, symbolic regression aims to find human-readable mathematical expressions that capture the underlying logic.

The discovered expressions provide insight into the mechanisms at play, allowing us to verify, critique, and build trust in the model.