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Investigates internal neural representations of cognitive complexity in Large Language Models using Bloom's Taxonomy, providing a novel evaluation framework beyond surface-level metrics. This matters for understanding and regulating AI decision-making. The approach is genuinely new as it applies linear probing to mechanistically interpret cognitive complexity. This can be applied to various domains, including education and AI safety.
“arXiv:2602.17229v2 Announce Type: replace Abstract: The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. This study investigates the internal neural represe…”
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ENTITY
Bloom's Taxonomy, Large Language Models, Linear Probing, arXiv:2602.17229v2
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Jul 21, 2026