Architecture-driven Shift: towards a lightweight selector for capturing the trends of logit shift

Researchers propose Architecture-driven Shift (ADS), a lightweight computational method to predict how pre-trained neural networks will perform in continual learning scenarios by measuring logit shift without expensive calculations. The approach theoretically decouples architecture characteristics from data dependency, achieving strong correlation with actual performance across 175+ diverse model architectures.

This research addresses a fundamental challenge in modern machine learning: efficiently selecting which pre-trained models best balance plasticity (ability to learn new tasks) and stability (retention of prior knowledge) in continual learning scenarios. Computing logit shift directly—the standard metric for this evaluation—requires prohibitive computational resources, especially when evaluating large numbers of candidate architectures. The authors bypass this bottleneck by developing a theoretical framework that isolates how architectural properties independently influence logit shift, separate from task-specific data effects.

The breakthrough stems from analyzing three mechanistic components: how weight gradients scale with layer width, the optimization path length for new tasks, and asymptotic task conflicts in wide networks. Rather than requiring full training or extensive data sampling, ADS captures logit shift tendency using minimal computational overhead and few data samples. Empirical validation across diverse architectures demonstrates remarkably consistent results, with Spearman correlation coefficients as low as 0.731 between ADS and actual logit shift—a robust relationship that holds across architectural heterogeneity where prior theoretical work failed.

For practitioners, this enables rapid model selection for continual learning applications without expensive computational trials. This efficiency gain matters significantly in industrial settings where evaluating hundreds of pre-trained models represents substantial cost. The practical application as a proxy for expected calibration error—critical for reliable AI systems—extends utility beyond academic research. Organizations deploying continual learning systems can now identify optimal base models orders of magnitude faster, reducing time-to-production for adaptive AI systems that must handle streaming data and task shifts without catastrophic forgetting.

• →ADS provides a computationally efficient alternative to direct logit shift measurement for continual learning model selection
• →The framework theoretically decouples architecture dependency from data dependency, explaining why model structure predicts learning stability
• →Strong monotonic correlation (r_s ≥ 0.731) across 175+ architectures validates ADS as a reliable performance proxy
• →Minimal data sampling requirements make ADS practical for evaluating large numbers of candidate pre-trained models
• →Application as expected calibration error proxy enables faster deployment of reliable continual learning systems