Support Vector Attention is introduced, a max-margin memory whose weights are support coefficients of a one-class SVM with fixed box parameter C that certifying output-preserving eviction and demonstrating surgical forgetting, exact editing, patient-record deletion, and a forgettable retrieval memory over real sentence embeddings.
The AdaPop (Adaptive Popularity) method is proposed, which combines local token confidence with a per-fact popularity-dependent exponent derived from an external proxy, and automates the forget-retain balance via a dual-ascent controller that adjusts the retain penalty each epoch.
Anna Borisiuk, A. Savchenko, Alexander Panchenko et al.· 0 citations
The Relearning Score (RS), which jointly measures forget-class recovery and retain-accuracy preservation, and report class-matched $\Delta$RS relative to a retrained reference, is introduced.
Zahra Dehghani, Pablo Piantanida, Mohammadhadi Shateri· 0 citations
This work proposes Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer-level unlearning framework that selects transformer layers using a forget-to-retain significance score and provides an empirical path toward quantization-resilient unlearning.
Ravi Ranjan, O. Kotevska, Agoritsa Polyzou· 1 citation
ADU is presented, a fine-grained, training-based framework that shifts unlearning from token erasure to contextual attention-pathway decoupling, and achieves the strongest aggregate performance among evaluated baselines on the TOFU and WMDP benchmarks.
Xun-Lei Chen, Qirui Ye, Yuang Li et al.· 0 citations
This work proposes ARIA (autoencoder-gated inference-time unlearning), a test-time unlearning method that leaves model weights intact and gates access to unwanted knowledge only when generation enters a forget-related state and introduces three post-unlearning adversarial attacks targeting weight-space and decoding-spa...
Ping-Zhi Li, Jinhao Duan, Vaishnav Tadiparthi et al.· 0 citations
The results suggest that the combination of sparse representations, local learning, and persistent memory is a promising direction for continual learning, while motivating further investigation into the respective roles of learning rules, representations, and architectural design in mitigating catastrophic forgetting.
