Adaptive anchoring under unknown or drifting target prevalence

Develop an adaptive prevalence-anchoring mechanism for OLIVE that remains valid when the target prevalence is unknown or drifts over time, rather than relying on the fixed prior ath]ath]\piath] used in the current framework.

Background

OLIVE currently uses a fixed target prevalence π\pi to anchor the mean of its item-level posterior beliefs. Although the paper argues that this anchoring preserves the ranking of items and therefore does not directly alter guidance, real deployments such as radiology, air-traffic control, and surveillance may involve unknown or changing base rates. The authors explicitly identify the need for adaptive anchoring beyond the fixed prevalence assumption as a harder open problem. The limitations section reiterates that the fixed anchor is untested under drifting prevalence and that an online prevalence estimator remains future work.

References

The one cell that stays open, a prevalence near 5\% with an excess of 1.5, leaves too few declared papers in the simulation to form an anchor, and no quarter the data prefer sits there.

— More than half of recent astronomy papers are written with language-model assistance  (2609.10664 - Saad et al., 9 Sep 2026) in Section “Inference and calibration,” subsection “Identification and the missing background” / injection-and-recovery analysis

The harder open problem is condition (1): unknown or drifting prevalence needs adaptive anchoring beyond OLIVE's fixed $\pi$; richer hierarchical interfaces would also extend OLIVE's binary posterior to multi-class beliefs, and visually subtle domains such as radiology would swap in a domain-pretrained frozen backbone (e.g., BiomedCLIP).

— Augmenting Human Performance with an XR Agent Learning from Online Behavior and BCI Evidence  (2608.30369 - Li et al., 31 Aug 2026) in Section Discussion and Future Work