{"total":18,"rows":[{"id":"2112.05224","title":"Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures","created":"2021-12-09","cited_by_count":73,"area":"safety"},{"id":"2401.06187","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","created":"2024-01-11","cited_by_count":52,"area":"safety"},{"id":"2402.18059","title":"Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models","created":"2024-02-28","cited_by_count":34,"area":"safety"},{"id":"2411.15537","title":"MUNBa: Machine Unlearning via Nash Bargaining","created":"2024-11-23","cited_by_count":17,"area":"safety"},{"id":"2503.02312","title":"Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization","created":"2025-03-04","cited_by_count":7,"area":"safety"},{"id":"2502.19770","title":"TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning","created":"2025-02-27","cited_by_count":6,"area":"safety"},{"id":"2510.22124","title":"Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery","created":"2025-10-25","cited_by_count":5,"area":"safety"},{"id":"2409.19173","title":"HM3: Heterogeneous Multi-Class Model Merging","created":"2024-09-27","cited_by_count":3,"area":"safety"},{"id":"2306.03600","title":"Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations","created":"2023-06-06","cited_by_count":1,"area":"safety"},{"id":"2509.25613","title":"SMS: Self-supervised Model Seeding for Verification of Machine Unlearning","created":"2025-09-30","cited_by_count":1,"area":"safety"},{"id":"2510.15976","title":"Learning to Watermark: A Selective Watermarking Framework for Large Language Models via Multi-Objective Optimization","created":"2025-10-13","cited_by_count":1,"area":"safety"},{"id":"2601.07214","title":"BlindU: Blind Machine Unlearning without Revealing Erasing Data","created":"2026-01-12","cited_by_count":1,"area":"safety"},{"id":"2502.19785","title":"SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications","created":"2025-02-27","cited_by_count":0,"area":"safety"},{"id":"2506.04712","title":"UNO: Unlearning via Orthogonalization in Generative models","created":"2025-06-05","cited_by_count":0,"area":"safety"},{"id":"2606.00399","title":"Multi-Objective Reference-Aligned Machine Unlearning","created":"2026-05-29","cited_by_count":0,"area":"safety"},{"id":"2606.16180","title":"To forget is to preserve: Machine Unlearning for 3D medical image segmentation","created":"2026-06-15","cited_by_count":0,"area":"safety"},{"id":"2606.17122","title":"TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations","created":"2026-06-15","cited_by_count":0,"area":"safety"},{"id":"2607.14194","title":"Inference-Time Concept Suppression and Video-Centric Evaluation for Text-to-Video Models","created":"2026-07-15","cited_by_count":0,"area":"safety"}],"next_cursor":null}