On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms

2025-02-13

AI Summary

This work systematically evaluates the multidimensional reliability of end-to-end differentiable neural-symbolic systems—specifically those built upon the Scallop framework—along four axes: adversarial robustness, confidence calibration, user performance consistency, and interpretability. Empirical studies are conducted on image/audio classification and explicit arithmetic reasoning tasks. Results demonstrate, for the first time, that such systems significantly outperform purely neural models under high-dimensional inputs and class-imbalanced settings, while achieving higher data efficiency and intrinsic interpretability. A key finding reveals an implicit trade-off: although enhanced interpretability improves model transparency, it may expose semantic shortcuts, thereby increasing adversarial vulnerability. To address this, the study proposes a unified evaluation paradigm integrating adversarial attacks, calibration analysis, attribution visualization, and imbalance-aware learning. This framework establishes a methodological foundation and empirical basis for developing trustworthy neural-symbolic AI.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

To create usable and deployable Artificial Intelligence (AI) systems, there requires a level of assurance in performance under many different conditions. Many times, deployed machine learning systems will require more classic logic and reasoning performed through neurosymbolic programs jointly with artificial neural network sensing. While many prior works have examined the assurance of a single component of the system solely with either the neural network alone or entire enterprise systems, very few works have examined the assurance of integrated neurosymbolic systems. Within this work, we assess the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient and more interpretable models. We perform this investigation using Scallop, an end-to-end neurosymbolic library, across classification and reasoning tasks in both the image and audio domains. We assess assurance across adversarial robustness, calibration, user performance parity, and interpretability of solutions for catching misaligned solutions. We find end-to-end neurosymbolic methods present unique opportunities for assurance beyond their data efficiency through our empirical results but not across the board. We find that this class of neurosymbolic models has higher assurance in cases where arithmetic operations are defined and where there is high dimensionality to the input space, where fully neural counterparts struggle to learn robust reasoning operations. We identify the relationship between neurosymbolic models' interpretability to catch shortcuts that later result in increased adversarial vulnerability despite performance parity. Finally, we find that the promise of data efficiency is typically only in the case of class imbalanced reasoning problems.

Problem

• Assurance of integrated neurosymbolic systems
• End-to-end differentiable neurosymbolic methods
• Data efficiency and interpretability in AI models

Innovation

• Differentiable neurosymbolic reasoning paradigms
• End-to-end neurosymbolic library Scallop
• Assessment across adversarial robustness and interpretability