# Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding

arXiv: https://arxiv.org/abs/2112.11805 · HTML: https://mlsanity.com/paper/2112.11805/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [Regression Concept Vectors for Bidirectional Explanations in Histopathology](https://mlsanity.com/paper/1904.04520/) (2019)
- [Exploring Neural Models for Parsing Natural Language into First-Order Logic](https://mlsanity.com/paper/2002.06544/) (2020)
- [Layerwise Knowledge Extraction from Deep Convolutional Networks](https://mlsanity.com/paper/2003.09000/) (2020)

## Built on by (most cited first, 3 of 3)

- [Interpretable Neural-Symbolic Concept Reasoning](https://mlsanity.com/paper/2304.14068/) (2023)
- [Two Axes of LLM Abstention: Answer Correctness and Question Answerability](https://mlsanity.com/paper/2607.08456/) (2026)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)

## Abstract (arXiv)

We propose neural-symbolic integration for abstract concept explanation and interactive learning. Neural-symbolic integration and explanation allow users and domain-experts to learn about the data-driven decision making process of large neural models. The models are queried using a symbolic logic language. Interaction with the user then confirms or rejects a revision of the neural model using logic-based constraints that can be distilled into the model architecture. The approach is illustrated using the Logic Tensor Network framework alongside Concept Activation Vectors and applied to a Convolutional Neural Network.