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Targeted low-rank repair can effectively detoxify backdoored LLMs without the need for full retraining, preserving benign behavior while suppressing malicious outputs.
Feature splitting in traditional Sparse Autoencoders can be mitigated by using learned subspace decoders, leading to improved interpretability and efficiency in mechanistic analysis.
