Machine unlearning, knowledge editing and removing hazardous capabilities.
Audits subspace-projection defenses against LoRA backdoors, showing they suppress static attack success without erasing the trigger, so backdoors reactivate within a few epochs of fine-tuning.
Proposes spectral unlearning, a matrix-structured optimizer update for LLM unlearning that improves memorization removal, privacy and utility trade-offs over existing baselines.
Proposes Hallucination-Guided Unlearning, using model hallucinations to localize and edit MLP neurons for targeted forgetting, reporting better forget-utility trade-offs on TOFU and WMDP.
Proposes a prompt-free audit of concept erasure in diffusion models, measuring residual concept probability via path-space KL cost. The barrier predicts leakage of NSFW and object concepts and guides a repair.
A survey of mechanistic interpretability for AI safety that covers its foundations and methods, proposes an evaluation framework, and assesses how understanding internals supports editing, steering and monitoring.
Shows that unlearned models can match retrained models on standard metrics yet encode different features, and proposes a feature-recovery measure that predicts relearning deviations.
Proposes VKCR, a parameter-efficient method that unlearns visual-to-knowledge associations in multimodal reasoning models, suppressing sensitive information under designated images while preserving permitted access, with new benchmarks.