The Metacognitive Monitoring Battery applied to 20 LLMs identifies three self-monitoring profiles, shows inverted accuracy and sensitivity ranks, and finds retrospective and prospective regulation largely dissociable.
arXiv preprint arXiv:2509.21545 (2025)
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A benchmark across 115 models shows that initial denial of preferences strongly predicts later denial of consciousness, while models still generate consciousness-themed content despite training to deny it.
Meta-d' and signal detection theory provide quantitative tools to assess metacognitive sensitivity and risk-based regulation in large language models.
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The Metacognitive Monitoring Battery: A Cross-Domain Benchmark for LLM Self-Monitoring
The Metacognitive Monitoring Battery applied to 20 LLMs identifies three self-monitoring profiles, shows inverted accuracy and sensitivity ranks, and finds retrospective and prospective regulation largely dissociable.
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A benchmark across 115 models shows that initial denial of preferences strongly predicts later denial of consciousness, while models still generate consciousness-themed content despite training to deny it.
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Meta-d' and signal detection theory provide quantitative tools to assess metacognitive sensitivity and risk-based regulation in large language models.