softreason-a-fully-differentiable-neuro-soft-symbolic-deductive-reasoning-architecture-over-high-dimensional-perceptual-data-7b39494e·1 events·first seen Aliases: SoftReason: A Fully Differentiable Neuro-Soft-Symbolic Deductive Reasoning Architecture over High-Dimensional Perceptual Data
A new arXiv preprint introduces SoftReason, a neuro-soft-symbolic architecture that eliminates the discrete gradient gap in classical neuro-symbolic pipelines by representing deductive state as a soft interpretation tensor over candidate constants and predicates. The system integrates probabilistic perception, Knowledge Graph evidence injection, and differentiable deductive closure into a single end-to-end trainable architecture. The core innovation is a learned differentiable lift of the immediate-consequence operator using predicate-definition embeddings. The framework is instantiated on Knowledge-aware Visual Question Answering (KVQA) as a proof-of-concept.