When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning

2025-10-27

AI Summary

Existing systematic relational reasoning benchmarks overly rely on the “relational path composition” assumption, leading to strong in-distribution performance but poor out-of-distribution generalization. To address this, we propose NoRA—the first systematic relational reasoning benchmark that makes no a priori assumptions about path-based reasoning structures. NoRA features multi-level difficulty tasks explicitly designed to challenge and transcend the path-composition paradigm. We introduce a path-agnostic evaluation protocol and construct diverse reasoning tasks using neural-symbolic methods, Transformer variants, and graph neural networks. Empirical results reveal substantial performance bottlenecks of mainstream models in non-path-based scenarios. NoRA systematically uncovers fundamental limitations of current approaches and establishes a more rigorous, generalizable evaluation standard—thereby advancing the development of models with genuine systematic generalization capability.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• Benchmarking systematic neural relational reasoning models
• Addressing limitations of path-based reasoning assumptions
• Introducing NoRA benchmark for improved generalization

Innovation

• Introducing NoRA benchmark for systematic reasoning
• Extending beyond path-based relational reasoning methods
• Adding multiple difficulty levels to neural models