MEDLEY-BENCH reveals an evaluation/control dissociation in AI metacognition where scale improves reflective scoring but not proportional belief revision, with a consistent knowing/doing gap across 35 models.
Fleming and Hakwan C
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Metacognition should serve as a core design principle for AI to improve accuracy, security, and efficiency, demonstrated via a federated learning case study and supported by a new software framework.
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MEDLEY-BENCH: Scale Buys Evaluation but Not Control in AI Metacognition
MEDLEY-BENCH reveals an evaluation/control dissociation in AI metacognition where scale improves reflective scoring but not proportional belief revision, with a consistent knowing/doing gap across 35 models.
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Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
Metacognition should serve as a core design principle for AI to improve accuracy, security, and efficiency, demonstrated via a federated learning case study and supported by a new software framework.