Antonin Sulc

Enforcing compositional structure in protein network embeddings yields a 10x boost in functional analogy accuracy, suggesting a new path to understanding biological function.

Forget discrete Kripke structures: this work uses Neural SDEs to lift modal logical reasoning to continuous manifolds, enabling logic-informed neural networks that learn from logical constraints without needing governing equations.