Human cognition excels at symbolic reasoning, deducing abstract rules from
limited samples. This has been explained using symbolic and connectionist
approaches, inspiring the development of a neuro-symbolic architecture that
combines both paradigms. In parallel, recent studies have proposed the use of a
"relational bottleneck" that separates object-level features from abstract
rules, allowing learning from limited amounts of data . While powerful, it is
vulnerable to the curse of compositionality meaning that object representations
with similar features tend to interfere with each other. In this paper, we
leverage hyperdimensional computing, which is inherently robust to such
interference to build a compositional architecture. We adapt the "relational
bottleneck" strategy to a high-dimensional space, incorporating explicit vector
binding operations between symbols and relational representations.
Additionally, we design a novel high-dimensional attention mechanism that
leverages this relational representation. Our system benefits from the low
overhead of operations in hyperdimensional space, making it significantly more
efficient than the state of the art when evaluated on a variety of test
datasets, while maintaining higher or equal accuracy.

人类认知在符号推理方面表现出色，从有限样本中推导出抽象规则。本文通过结合符号和连接主义方法，提出了一种神经符号化结构；同时，在学习有限数据量时，采用了将对象级特征与抽象规则分离的 “关系瓶颈” 策略。此外，本文借助于鲁棒性更强的高维计算方法来构建具有组合性的体系结构，并设计了一种新颖的高维注意机制。相较于现有技术，在各种测试数据集上，本系统具有更高的效率且保持相同或更高的准确性。