Enhancing Metacognitive AI: Knowledge-Graph Population with Graph-Theoretic LLM Enrichment
The article discusses a new system called MetaKGEnrich that enhances metacognitive abilities in AI. This system automates the process of knowledge graph population and self-directed knowledge repair in large language models. The results show significant improvements in answer quality across multiple datasets, indicating progress towards humanlike metacognitive learning in AI.
- ▪MetaKGEnrich builds knowledge graphs from seed queries and detects sparse regions using graph metrics.
- ▪The system utilizes GPT-4o to generate targeted questions and retrieves web evidence for knowledge enhancement.
- ▪Improvements in answer quality were observed in 80% of HotpotQA questions, 87% of Google Research Natural Questions, and 83% of MS MARCO questions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16676 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.16676 (cs) [Submitted on 15 May 2026] Title:Enhancing Metacognitive AI: Knowledge-Graph Population with Graph-Theoretic LLM Enrichment Authors:Deniz Askin, Gal Hadar, Brendan Conway-Smith View a PDF of the paper titled Enhancing Metacognitive AI: Knowledge-Graph Population with Graph-Theoretic LLM Enrichment, by Deniz Askin and 2 other authors View PDF Abstract:Metacognition-the ability to monitor one's own knowledge state, spot gaps, and autonomously fill them--remains largely absent from modern AI. Here, we present MetaKGEnrich, a fully automated pipeline that endows large language model (LLM) applications with self-directed knowledge repair.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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