Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
Fuente:
arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866917499057995776 |
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| author | Chuprov, Sergei Lange, Richard D. Reznik, Leon Shakarian, Paulo Zatsarenko, Raman Korobeinikov, Dmitrii |
| author_facet | Chuprov, Sergei Lange, Richard D. Reznik, Leon Shakarian, Paulo Zatsarenko, Raman Korobeinikov, Dmitrii |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15567 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI Chuprov, Sergei Lange, Richard D. Reznik, Leon Shakarian, Paulo Zatsarenko, Raman Korobeinikov, Dmitrii Artificial Intelligence 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. |
| title | Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.15567 |