# Neurosymbolic AI -- Why, What, and How

arXiv: https://arxiv.org/abs/2305.00813 · HTML: https://mlsanity.com/paper/2305.00813/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 4 of 4)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety](https://mlsanity.com/paper/2206.13349/) (2022)
- [Process Knowledge-infused Learning for Suicidality Assessment on Social Media](https://mlsanity.com/paper/2204.12560/) (2022)

## Built on by (most cited first, 19 of 19)

- [ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance](https://mlsanity.com/paper/2305.08010/) (2023)
- [World Models: The Safety Perspective](https://mlsanity.com/paper/2411.07690/) (2024)
- [Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes](https://mlsanity.com/paper/2503.12286/) (2025)
- [A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives](https://mlsanity.com/paper/2503.18213/) (2025)
- [AI Mother Tongue: Self-Emergent Communication in MARL via Endogenous Symbol Systems](https://mlsanity.com/paper/2507.10566/) (2025)
- [Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust](https://mlsanity.com/paper/2305.04989/) (2023)
- [Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes](https://mlsanity.com/paper/2306.01805/) (2023)
- [Knowledge-Infused Self Attention Transformers](https://mlsanity.com/paper/2306.13501/) (2023)
- [IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations](https://mlsanity.com/paper/2306.13865/) (2023)
- [Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models](https://mlsanity.com/paper/2405.14012/) (2024)
- [SECURE: Semantics-aware Embodied Conversation under Unawareness for Lifelong Robot Learning](https://mlsanity.com/paper/2409.17755/) (2024)
- [In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes](https://mlsanity.com/paper/2602.14318/) (2026)
- [Neuro-symbolic Weak Supervision: Theory and Semantics](https://mlsanity.com/paper/2503.18509/) (2025)
- [SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing](https://mlsanity.com/paper/2505.06492/) (2025)
- [Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting](https://mlsanity.com/paper/2512.14288/) (2025)
- [Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena](https://mlsanity.com/paper/2604.22990/) (2026)
- [High Quality Embeddings for Horn Logic Reasoning](https://mlsanity.com/paper/2605.20467/) (2026)
- [Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention](https://mlsanity.com/paper/2608.26168/) (2026)
- [Bringing AI to Autonomous Systems -- From Cognition to Collective Intelligence](https://mlsanity.com/paper/2609.30291/) (2026)

## Abstract (arXiv)

Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of AI, refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision-making in safety-critical applications such as healthcare, criminal justice, and autonomous driving. This article introduces the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and knowledge-guided symbolic approaches to create more capable and flexible AI systems. These systems have immense potential to advance both algorithm-level (e.g., abstraction, analogy, reasoning) and application-level (e.g., explainable and safety-constrained decision-making) capabilities of AI systems.