Calibration Is Not Control: Intervention Value for LLM-Agent Oversight
Quick summary
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,
Key takeaways
- 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.
Why it matters
“Calibration Is Not Control: Intervention Value for LLM-Agent Oversight” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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