# Metacognitive AI: Framework and the Case for a Neurosymbolic Approach

arXiv: https://arxiv.org/abs/2406.12147 · HTML: https://mlsanity.com/paper/2406.12147/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [A Semantic Loss Function for Deep Learning with Symbolic Knowledge](https://mlsanity.com/paper/1711.11157/) (2017)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [Coherent Hierarchical Multi-Label Classification Networks](https://mlsanity.com/paper/2010.10151/) (2020)
- [Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning](https://mlsanity.com/paper/2209.08483/) (2022)
- [Uncertainty Regularized Evidential Regression](https://mlsanity.com/paper/2401.01484/) (2024)
- [The Roles of Symbols in Neural-based AI: They are Not What You Think!](https://mlsanity.com/paper/2304.13626/) (2023)
- [Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control](https://mlsanity.com/paper/2307.12388/) (2023)
- [Explaining Deep Learning Hidden Neuron Activations using Concept Induction](https://mlsanity.com/paper/2301.09611/) (2023)

## Built on by (most cited first, 9 of 9)

- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Truly Self-Improving Agents Require Intrinsic Metacognitive Learning](https://mlsanity.com/paper/2506.05109/) (2025)
- [Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses](https://mlsanity.com/paper/2502.15365/) (2025)
- [Probabilistic Foundations for Metacognition via Hybrid-AI](https://mlsanity.com/paper/2502.05398/) (2025)
- [Metacognition in LLMs: Foundations, Progress, and Opportunities](https://mlsanity.com/paper/2607.11881/) (2026)
- [Abduction of Domain Relationships from Data for VQA](https://mlsanity.com/paper/2502.09219/) (2025)
- [Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy](https://mlsanity.com/paper/2602.11897/) (2026)
- [Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI](https://mlsanity.com/paper/2605.15567/) (2026)
- [ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning](https://mlsanity.com/paper/2605.16309/) (2026)

## Abstract (arXiv)

Metacognition is the concept of reasoning about an agent's own internal processes and was originally introduced in the field of developmental psychology. In this position paper, we examine the concept of applying metacognition to artificial intelligence. We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-turn and explore how neurosymbolic AI (NSAI) can be leveraged to address challenges of metacognition.