# Reflective Artificial Intelligence

arXiv: https://arxiv.org/abs/2301.10823 · HTML: https://mlsanity.com/paper/2301.10823/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 127 references that are in the map.
- First posted on arXiv 25 Jan 2023.

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

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [The Dark Side of Ethical Robots](https://mlsanity.com/paper/1606.02583/) (2016)

## Built on by (most cited first, 4 of 4)

- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Reflective Linguistic Programming (RLP): A Stepping Stone in Socially-Aware AGI (SocialAGI)](https://mlsanity.com/paper/2305.12647/) (2023)
- [The challenge of uncertainty quantification of large language models in medicine](https://mlsanity.com/paper/2504.05278/) (2025)
- [Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs](https://mlsanity.com/paper/2506.00072/) (2025)

## Abstract (arXiv)

Artificial Intelligence (AI) is about making computers that do the sorts of things that minds can do, and as we progress towards this goal, we tend to increasingly delegate human tasks to machines. However, AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is utterly missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward.