The minimax rate for estimating calibration error is Theta((L epsilon/m)^{1/3}), creating a verification tax that makes auditing harder as models improve.
On calibration of modern neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Self-monitoring modules in multi-timescale agents fail as auxiliary losses due to collapse but show limited gains when wired into policy decisions, without outperforming simple baselines.
citing papers explorer
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The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime
The minimax rate for estimating calibration error is Theta((L epsilon/m)^{1/3}), creating a verification tax that makes auditing harder as models improve.
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Self-Monitoring Benefits from Structural Integration: Lessons from Metacognition in Continuous-Time Multi-Timescale Agents
Self-monitoring modules in multi-timescale agents fail as auxiliary losses due to collapse but show limited gains when wired into policy decisions, without outperforming simple baselines.