A Multi-Resolution Benchmark Framework for Spatial Reasoning Assessment in Neural Networks

2025-08-18

AI Summary

Neural networks exhibit systematic deficiencies in reasoning about fundamental spatial morphological properties—such as connectivity and metric relationships—underpinning geometric and topological understanding. Method: We introduce the first scalable, multi-resolution benchmark framework comprising two synthetically generated datasets—maze connectivity and spatial distance—produced via VoxLogicA to ensure topological consistency; we integrate nnU-Net with Dice and IoU metrics to automate the full pipeline from data generation and model inference to quantitative evaluation. Contribution/Results: This framework enables, for the first time under a unified protocol, systematic quantification of neural network failure modes across multi-scale spatial tasks. Empirical validation confirms its efficacy in diagnosing architectural limitations of deep learning models. The benchmark provides a reproducible foundation for advancing neuro-symbolic hybrid methods and enhancing spatial robustness in clinical image analysis.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

This paper presents preliminary results in the definition of a comprehensive benchmark framework designed to systematically evaluate spatial reasoning capabilities in neural networks, with a particular focus on morphological properties such as connectivity and distance relationships. The framework is currently being used to study the capabilities of nnU-Net, exploiting the spatial model checker VoxLogicA to generate two distinct categories of synthetic datasets: maze connectivity problems for topological analysis and spatial distance computation tasks for geometric understanding. Each category is evaluated across multiple resolutions to assess scalability and generalization properties. The automated pipeline encompasses a complete machine learning workflow including: synthetic dataset generation, standardized training with cross-validation, inference execution, and comprehensive evaluation using Dice coefficient and IoU (Intersection over Union) metrics. Preliminary experimental results demonstrate significant challenges in neural network spatial reasoning capabilities, revealing systematic failures in basic geometric and topological understanding tasks. The framework provides a reproducible experimental protocol, enabling researchers to identify specific limitations. Such limitations could be addressed through hybrid approaches combining neural networks with symbolic reasoning methods for improved spatial understanding in clinical applications, establishing a foundation for ongoing research into neural network spatial reasoning limitations and potential solutions.

Problem

• Evaluating neural networks' spatial reasoning on connectivity and distance
• Assessing scalability and generalization across multiple resolutions
• Identifying limitations in geometric and topological understanding tasks

Innovation

• Multi-resolution benchmark for spatial reasoning assessment
• Synthetic datasets generated via VoxLogicA model checker
• Hybrid neural-symbolic methods for clinical applications