You can now train neural networks that perform logical reasoning directly on images without losing gradient information at the perception-to-logic boundary—the system learns to ground visual facts and apply knowledge graph rules in one differentiable pass.
SoftReason bridges perception and symbolic reasoning by making deductive logic fully differentiable. Instead of converting images to discrete symbols (which breaks gradient flow), it represents reasoning as soft probability tensors over candidate facts and predicates, allowing end-to-end training from pixels through logical deduction.