Automatic calibration of diagnostic smoothing and gating parameters

Develop systematic methods to automatically calibrate the smoothing coefficients and gating sensitivity parameters used in the bias–noise–alignment diagnostic-driven adaptive learning framework, eliminating manual tuning while maintaining stability across tasks.

Background

The proposed framework introduces smoothing (e.g., EMA coefficients) and gating sensitivity parameters that modulate learning based on bias–noise–alignment diagnostics. Although these parameters are reported to be more stable across tasks than raw learning rates, they currently require manual selection.

The authors explicitly state that finding systematic, automatic calibration methods for these parameters remains unresolved, highlighting a need for principled procedures that preserve the framework’s stability and interpretability without manual intervention.

References

Although the framework reduces reliance on manual learning-rate scheduling, it introduces sensitivity parameters controlling smoothing and gating strength. While these parameters tend to be more stable across tasks than raw learning rates, systematic methods for their automatic calibration remain an open problem.

— Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics  (2512.24445 - Samanta et al., 30 Dec 2025) in Section 7: Unified Perspective, Implications, and Limitations

We present this as a direction rather than a demonstration. The attribution framework is established here and the adaptation scaling laws are established separately, but closing the loop end to end on hardware, within the controllability and model-fidelity assumptions each component requires, remains open.

— Beyond sensitivity: mechanism-resolved error budgets for designing quantum sensors  (2608.28519 - Leclerc et al., 28 Aug 2026) in Supplementary Information, Section 10, “Outlook: from mechanism attribution to adaptive, meta-learned control,” paragraph “Status”