arXiv:2607. 05726v1 Announce Type: cross Abstract: Association unlearning aims to disable learned label-attribute shortcuts while preserving task performance.
By Amy Lu, Changxiu Ji
arXiv:2605. 27569v2 Announce Type: replace Abstract: Machine unlearning aims to remove the influence of specific training records from a deployed model without retraining from scratch.
By Georgina Cosma, Axel Finke
arXiv:2606. 25001v1 Announce Type: new Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced logit-level membership inference.
By Teresa Pui Yee Yong, Win Kent Ong, Chee Seng Chan
arXiv:2607. 09236v1 Announce Type: new Abstract: Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety.
By Amit Peleg, Naman Deep Singh, Naama Pearl, Bibhabasu Mohapatra, Matthias Hein
arXiv:2606. 07688v1 Announce Type: cross Abstract: Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs).
By Ziheng Chen, Jiali Cheng, Zezhong Fan, Hadi Amiri, Diyuan Wu, Gabriele Tolomei, Yang Zhang
arXiv:2505. 13353v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long code context or rely on pattern matching shortcuts remains unclear.
By Adam \v{S}torek, Mukur Gupta, Samira Hajizadeh, Prashast Srivastava, Suman Jana