The paper investigates how to provide verifiable deletion certificates for a dense key–value context memory used in support‑vector‑based readouts. By assigning explicit weights to keys and using a one‑class support‑vector boundary, the authors show that reserve keys can be removed without re‑solving, while active keys can be deleted with a decremental solver that matches the result of a full re‑solve. Extensive experiments on synthetic, near‑duplicate, clinical, and learned key sets demonstrate that maintained deletion achieves the same reference state as re‑solve, with negligible readout disagreement and significant speedups.
By Vishwajith Ramesh
The paper investigates what a service must retain to faithfully reproduce a training pipeline after data deletion requests, focusing on a rule where earlier similar records block later ones. It shows that a limit on future deletions precisely determines which records can still be needed for training, enabling an exact repair of training statistics after repeated deletions. The authors also construct finite‑precision features for ridge regression and demonstrate that the required private information can be minimized across a sequence of deletion requests, validating their approach on 1,001 AskUbuntu records.
By Priyank Jayraj, Devanshu sahoo, Uday Mittal
arXiv:2607. 19442v1 Announce Type: cross Abstract: Machine unlearning is commonly evaluated by matching a retrained oracle on trained probes.
By Sen Yang, Yuen-Hei Yeung
The paper investigates whether a model that has undergone class unlearning can still recover forgotten classes without access to original data. It introduces a white‑box audit method that generates synthetic probes in representation space, filters them by confidence, and relabels boundary‑adjacent probes as the forgotten class. The authors define a Relearning Score to quantify recovery while preserving retain performance, and demonstrate that several unlearning techniques on CIFAR‑10, CIFAR‑100, and TinyImageNet can be fully recovered in a source‑free setting, sometimes even outperforming a retrained reference.
By Zahra Dehghani, Pablo Piantanida, Mohammadhadi Shateri
arXiv:2609. 04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every serving API, a KV cache.
By Chao Yao, Yangbo Wei, Zhen Huang, Junhong Qian, Chenle Chen, Shaoqiang Lu, Chen Wu, Lei He
arXiv:2508. 12220v2 Announce Type: replace-cross Abstract: Can a prospectively instrumented training continuation reproduce a deletion counterfactual exactly after selected examples leave its replay dataset?
By Abdullah X