arXiv:2606.25165v1 Announce Type: new Abstract: Accuracy degradation is the standard metric for Catastrophic Forgetting (CF), however, it records only whether forgetting occurred or not. It saturates at the extremes and collapses discretely at task boundaries, hiding the internal structure of what is being forgotten. We introduce six softmax-derived metrics spanning true-label rank (TLR), predictive confidence, and distributional divergence that characterize forgetting continuously, each normalized to [0, 1] with no modification to training. On CIFAR-100, these metrics carry information where
The continuous evolution of AI models demands increasingly sophisticated metrics to understand and mitigate fundamental challenges like catastrophic forgetting, pushing researchers to develop more granular analytical tools.
Improved metrics for continual learning directly impact the robustness and reliability of AI agents and complex AI systems, enabling more stable and adaptable deployments in real-world environments.
The ability to continuously monitor and characterize forgetting more precisely will allow for targeted interventions in AI model training, moving beyond simple accuracy degradation to understand the 'how' and 'what' of model performance issues.
- · AI researchers
- · Developers of continual learning systems
- · Sectors deploying complex AI agents
- · AI ethics and safety researchers
- · Developers relying on outdated or coarse evaluation metrics
- · AI systems failing due to undetected catastrophic forgetting
More resilient and adaptable AI models are developed as a direct result of better diagnostic tools for learning and forgetting.
The improved stability of AI models will accelerate their integration into critical applications, relying on continuous learning and adaptation.
Advanced diagnostic capabilities could lead to new theoretical understandings of intelligence and learning, moving beyond current paradigms.
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Read at arXiv cs.LG