  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.
