Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
Abstract
This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI. The metacognitive solution involves systems monitoring their own states and judiciously allocating resources depending on each problem instance's difficulty or cost of mistakes. Drawing inspiration both from past work on resource-rational AI and from well-documented metacognitive strategies in psychology and cognitive science, we identify specific challenges in embedding these strategies into AI design and highlight open theoretical and implementation problems. We showcase these principles through a tangible example of improved learning efficiency, effectiveness, and security in a Federated Learning (FL) case study. We show how these principles can be translated into practice with a novel software framework developed specifically to allow the community to design, deploy, and experiment with metacognition-enabled AI applications.
Cite
@article{arxiv.2605.15567,
title = {Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI},
author = {Sergei Chuprov and Richard D. Lange and Leon Reznik and Paulo Shakarian and Raman Zatsarenko and Dmitrii Korobeinikov},
journal= {arXiv preprint arXiv:2605.15567},
year = {2026}
}
Comments
This is a preliminary version accepted for presentation and publication at the 43rd International Conference on Machine Learning (ICML26). The modified final version will be available in the conference proceedings