Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge. This study focuses on efficiently solving Raven's progressive matrices (RPM), a visual test for assessing abstract reasoning abilities, by using distributed computation and operators provided by vector-symbolic architectures (VSA). Instead of hard-coding the rule formulations associated with RPMs, our approach can learn the VSA rule formulations (hence the name Learn-VRF) with just one pass through the training data. Yet, our approach, with compact parameters, remains transparent and interpretable. Learn-VRF yields accurate predictions on I-RAVEN's in-distribution data, and exhibits strong out-of-distribution capabilities concerning unseen attribute-rule pairs, significantly outperforming pure connectionist baselines including large language models. Our code is available at https://github.com/IBM/learn-vector-symbolic-architectures-rule-formulations.

翻译：抽象推理是人类智能的基石，而用人工智能（AI）复现这一能力仍是持续挑战。本研究聚焦于通过向量符号架构（VSA）提供的分布式计算与算子，高效解决用于评估抽象推理能力的瑞文推理测验（RPM）。我们的方法无需硬编码RPM关联的规则公式，仅需单次遍历训练数据即可学习VSA规则公式（故称Learn-VRF）。尽管参数量精简，该方法仍保持透明性与可解释性。Learn-VRF在I-RAVEN分布内数据上实现精准预测，并在未见属性-规则对场景下展现出强大的分布外泛化能力，显著优于包括大语言模型在内的纯连接主义基线。代码已开源：https://github.com/IBM/learn-vector-symbolic-architectures-rule-formulations。