# NEUROLOGIC: From Neural Representations to Interpretable Logic Rules

arXiv: https://arxiv.org/abs/2501.08281 · HTML: https://mlsanity.com/paper/2501.08281/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias](https://mlsanity.com/paper/2004.12265/) (2020)
- [Dissecting Contextual Word Embeddings: Architecture and Representation](https://mlsanity.com/paper/1808.08949/) (2018)
- [CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs](https://mlsanity.com/paper/2304.05207/) (2023)

## Built on by (most cited first, 1 of 1)

- [J-Miner: Recovering the Decision Logic of Fine-Tuned LLM Classifiers as Compact Rules](https://mlsanity.com/paper/2608.17063/) (2026)

## Abstract (arXiv)

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.