Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
The paper discusses the necessity of metacognition in artificial intelligence design. It proposes that AI systems should monitor their own states and allocate resources based on problem difficulty. The authors present a case study demonstrating improved learning efficiency and security through a metacognitive approach.
- ▪The paper argues for metacognition as a design principle for AI.
- ▪It highlights challenges in embedding metacognitive strategies into AI systems.
- ▪A case study showcases improved learning efficiency in Federated Learning.
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Computer Science > Artificial Intelligence arXiv:2605.15567 (cs) [Submitted on 15 May 2026] Title:Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI Authors:Sergei Chuprov, Richard D. Lange, Leon Reznik, Paulo Shakarian, Raman Zatsarenko, Dmitrii Korobeinikov View a PDF of the paper titled Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI, by Sergei Chuprov and 5 other authors View PDF 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.
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