Catastrophic Forgetting as Accessibility Collapse: A Three-Level Framework for Knowledge Persistence in Continual Learning
arXiv:2606.06032v1 Announce Type: new Abstract: Catastrophic forgetting is commonly interpreted as the irreversible erasure of previously acquired knowledge during sequential learning. In this work, we investigate an alternative perspective: that forgetting may arise not from complete destruction of task representations but from a loss of accessibility to preserved information. We introduce a three-level framework separating knowledge storage, representation, and accessibility, and evaluate each component through a series of continual-learning experiments on sequential CIFAR-100 classification
This research provides a timely new perspective on a fundamental challenge in continual learning, potentially unlocking more robust and efficient AI systems.
Understanding catastrophic forgetting not as data erasure but as accessibility loss could lead to more stable and adaptable AI, critical for real-world deployment.
The paradigm shifts from preventing knowledge destruction to designing better methods for knowledge retrieval and integration in continually learning AI models.
- · AI research labs
- · Developers of continual learning systems
- · Industries deploying AI with evolving data streams
- · AI models prone to rapid obsolescence
- · Legacy continual learning approaches relying solely on explicit knowledge replay
Improved stability and longevity of AI models in dynamic environments.
Reduced computational resources for re-training as knowledge is preserved rather than re-learned, impacting the compute supply chain.
Accelerated development of sophisticated AI agents capable of long-term learning and adaptation without constant architectural overhauls.
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Read at arXiv cs.LG