在许多推理问题, 中，前提并不是作为离散符号, 观察到的，而是必须从高维输入中推断出来。此外,谓词词汇,参数结构,和可信证据由知识图(KG),或规则定义提供。经典的神经符号管道在感知和演绎之间有一个离散的接口。我们提出了一种神经软符号架构，用于对潜在的感知事实和知识提供的谓词进行可微的演绎推理。 SoftReason 通过将演绎状态表示为候选常量和谓词上的局部软解释张量来消除梯度间隙。 Perception提出概率基础事实, KG三元组作为高置信度软证据,输入，并且每个查询锚,谓词选择,和闭包更新保持可微分。我们的核心创新是立即后果运算符的学习可微提升。它使用谓词定义嵌入和潜在组合通道来形成软体谓词混合物,聚合所有可能的见证,提出查询条件的头部事实,并通过单调概率OR更新解释。我们实例化了知识感知视觉问答 (KVQA), 框架，并演示了 SoftReason 如何在一个可训练架构中支持端到端感知基础, KG 证据注入, 和可微演绎闭包。
In many reasoning problems, the premises are not observed as discrete symbols, but must be inferred from high-dimensional inputs. Further, the predicate vocabulary, argument structure, and trusted evidence are supplied by a Knowledge Graph (KG), or rule definitions. Classical neuro-symbolic pipelines have a discrete interface between perception and deduction. We present a neuro-soft-symbolic architecture for differentiable deductive reasoning over latent perceptual facts and knowledge-provided predicates. SoftReason removes the gradient gap by representing the deductive state as a local soft interpretation tensor over candidate constants and predicates. Perception proposes probabilistic base facts, KG triples enter as high-confidence soft evidence, and every query anchor, predicate choice, and closure update remains differentiable. Our core innovation is a learned differentiable lift of the immediate-consequence operator. It uses predicate-definition embeddings and latent composition channels to form soft body-predicate mixtures, aggregate over all possible witnesses, propose query-conditioned head facts, and update the interpretation through a monotone probabilistic OR. We instantiate the framework on Knowledge-aware Visual Question Answering (KVQA), and demonstrates how SoftReason supports end-to-end perceptual grounding, KG evidence injection, and differentiable deductive closure in one trainable architecture.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)