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This paper argues that catastrophic forgetting in neural networks is not erasure but an interface alignment problem. It introduces 'transport keys' to recover latent task-specific features from sequentially trained models, demonstrating significant performance recovery on split CIFAR-100.
MechELK is a three-stage framework combining mechanistic interpretability tools (SAE, activation patching, causal probing) with representation engineering to elicit latent knowledge from LLMs, achieving 84.7% accuracy and outperforming existing methods like CCS and linear probing.