Compositional Neuro-Symbolic Reasoning

1. 1Extract object-level structure from grids using neural networks
2. 2Propose candidate transformations using neural priors
3. 3Apply symbolic reasoning to transform and abstract the extracted structures
4. 4Evaluate and refine the model using the Abstraction and Reasoning Corpus (ARC)

AI engineers and researchers on a team can benefit from this approach to improve the reliability and generalization of their models, while data scientists can apply these techniques to complex problem-solving

💡 Neuro-symbolic architectures can leverage the strengths of both neural and symbolic approaches to achieve reliable combinatorial generalization

💡 Compositional Neuro-Symbolic Reasoning combines neural & symbolic approaches for improved abstraction-based reasoning