SoftReason：一种用于高维感知数据的完全可微神经软符号演绎推理架构
SoftReason: A Fully Differentiable Neuro-Soft-Symbolic Deductive Reasoning Architecture over High-Dimensional Perceptual Data
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中文总结 AI 辅助
研究针对前提需从高维输入推断且由知识图谱提供相关信息的推理问题，提出神经软符号架构SoftReason，核心是对直接后果算子可微提升，能在知识感知视觉问答中支持多种功能。
中文摘要 AI 辅助
在许多推理问题中，前提并非作为离散符号被观察到，而是必须从高维输入中推断出来。此外，谓词词汇、论证结构和可信证据由知识图谱（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离散接口。我们提出了一种神经软符号架构，用于对潜在感知事实和知识提供的谓词进行可微演绎推理。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差距。感知提出概率性基本事实，KG三元组作为高置信度软证据进入，每个查询锚点、谓词选择和闭包更新都是可微的。我们的核心创新是对直接后果算子的可微提升。它使用谓词定义嵌入和潜在组合通道来形成软主体谓词混合，在所有可能的见证上聚合，提出查询条件头部事实，并通过单调概率或运算更新解释。我们在知识感知视觉问答（KVQA）上实例化了该框架，并展示了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。
英文摘要
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.
发表机构
- Clemson University（克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。
