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
arXiv:2607. 23806v1 Announce Type: cross Abstract: Improving a language model today means retraining it: enormous compute, a new opaque model each cycle, non-deterministic output.
By Sietse Schelpe (Corbenic AI)
arXiv:2607. 12204v2 Announce Type: replace Abstract: Auditable memory requires a precise contract: which output is preserved, relative to which reference solve, and across which updates.
By Vishwajith Ramesh
arXiv:2610.34778v1 Announce Type: new
Abstract: Financial instruction-data pipelines must distinguish content validity from permission to release a particular dataset. Arya Forge combines typed recor...
By Kushagra Bhatnagar, Deekshith Marla
The paper introduces Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer‑selective unlearning framework for large language models. FOM-UL uses a forget‑to‑retain significance score to identify transformer layers that strongly influence the forget set while being insensitive to the retain set, allowing targeted updates that preserve most of the model. Experiments on TOFU, KnowUnDo, and MUSE-style benchmarks show that FOM-UL reduces residual memorization and maintains utility better than several baselines, even after 8‑bit and 4‑bit post‑training quantization, and it also limits recovery of forgotten content in adversarial prompt tests.
By Ravi Ranjan, Olivera Kotevska, Agoritsa Polyzou
The paper investigates how federated unlearning systems that broadcast updated linear classifiers after each client update can inadvertently leak the compact, additive summaries used for deletion. By submitting known changes and analyzing the returned classifiers, an attacker can recover the deleted sample’s class or even reinstate it. Experiments on MNIST and CIFAR‑10 show that high‑precision broadcasts enable exact label recovery, while lower precision limits fine‑grained recovery and diverse responses can prevent identification.
By Yijun Quan, Giovanni Montana
arXiv:2603. 12977v3 Announce Type: replace Abstract: Foundation models are commonly deployed as frozen feature extractors with a small trainable head to adapt to private, user-generated data in federated settings.
By Yijun Quan, Wentai Wu, Giovanni Montana