Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI

Computer Science > Artificial Intelligence [Submitted on 15 May 2026] Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI Sergei Chuprov, Richard D. Lange, Leon Reznik, Paulo Shakarian, Raman Zatsarenko, Dmitrii Korobeinikov 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. 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 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.15567 [cs.AI]   (or arXiv:2605.15567v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.15567 Focus to learn more Submission history From: Sergei Chuprov [view email] [v1] Fri, 15 May 2026 03:17:02 UTC (868 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)