Designing models that can learn to reason in a systematic way is an important and long-standing challenge. In recent years, a wide range of solutions have been proposed for the specific case of systematic relational reasoning, including Neuro-Symbolic approaches, variants of the Transformer architecture, and specialised Graph Neural Networks. However, existing benchmarks for systematic relational reasoning focus on an overly simplified setting, based on the assumption that reasoning can be reduced to composing relational paths. In fact, this assumption is hard-baked into the architecture of several recent models, leading to approaches that can perform well on existing benchmarks but are difficult to generalise to other settings. To support further progress in the field of systematic relational reasoning with neural networks, we introduce NoRA, a new benchmark which adds several levels of difficulty and requires models to go beyond path-based reasoning.
@article{arxiv.2510.23532,
title = {When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning},
author = {Anirban Das and Irtaza Khalid and Rafael Peñaloza and Steven Schockaert},
journal= {arXiv preprint arXiv:2510.23532},
year = {2025}
}