Anqi Xing | Searcharxiv

arXiv2025

VISIONLOGIC: From Neuron Activations to Causally Grounded Concept Rules for Vision Models

While concept-based explanations improve interpretability over local attributions, they often rely on correlational signals and lack causal validation. We introduce VisionLogic, a novel neural-symbolic framework that produces faithful, hierarchical explanations as global logical rules over causally validated concepts. VisionLogic first learns activation thresholds that abstract neuron activations into predicates, then induces class-level logical rules from these predicates. It then grounds predicates to visual concepts via ablation-based causal tests with iterative region refinement, ensuring that discovered concepts correspond to features that are causal for predicate activation. Across different vision architectures such as CNNs and ViTs, it produces interpretable concepts and compact rules that largely preserve the original model's predictive performance. In our large-scale human evaluations, VisionLogic's concept explanations significantly improve participants' understanding of model behavior over prior concept-based methods.

cs.CV↗

arXiv2025

NEUROLOGIC: From Neural Representations to Interpretable Logic Rules

Rule-based explanation methods offer rigorous and globally interpretable insights into neural network behavior. However, existing approaches are mostly limited to small fully connected networks and depend on costly layerwise rule extraction and substitution processes. These limitations hinder their generalization to more complex architectures such as Transformers. Moreover, existing methods produce shallow, decision-tree-like rules that fail to capture rich, high-level abstractions in complex domains like computer vision and natural language processing. To address these challenges, we propose NEUROLOGIC, a novel framework that extracts interpretable logical rules directly from deep neural networks. Unlike previous methods, NEUROLOGIC can construct logic rules over hidden predicates derived from neural representations at any chosen layer, in contrast to costly layerwise extraction and rewriting. This flexibility enables broader architectural compatibility and improved scalability. Furthermore, NEUROLOGIC supports richer logical constructs and can incorporate human prior knowledge to ground hidden predicates back to the input space, enhancing interpretability. We validate NEUROLOGIC on Transformer-based sentiment analysis, demonstrating its ability to extract meaningful, interpretable logic rules and provide deeper insights-tasks where existing methods struggle to scale.

cs.LG↗