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arXiv — cs.AI preprintsInternational7 October 2026

Calibration Is Not Control: Intervention Value for LLM-Agent Oversight

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arXiv:2606.21399v2 Announce Type: replace Abstract: Runtime oversight often intervenes when an LLM agent's calibrated failure score crosses a threshold. Yet states with the same failure risk can differ in whether intervention helps. Strictly increasing recalibration preserves the threshold policy class and cannot recover this distinction. We formalize when a summary is sufficient for intervention decisions and the utility lost when it is not. We evaluate the consequences by replaying agent prefixes and executing alternative actions from the same state. On ALFWorld, holding features, estimator,
— arXiv — cs.AI preprints

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