arXiv AI
Aug 5
Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning
arXiv:2608. 03123v1 Announce Type: cross Abstract: Machine unlearning aims to eliminate the influence of sensitive data on a model.
Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning
Machine unlearning aims to eliminate the influence of sensitive data on a model. In the real world, unlearning requests arrive continually, which gives rise to two challenges.
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arXiv AI
Jun 3
PURGE: Projected Unlearning via Retain-Guided Erasure
arXiv:2606. 03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems.
arXiv Machine Learning
Aug 3
When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks
arXiv:2607. 28829v1 Announce Type: cross Abstract: Self-improving federated agent networks keep training after deployment by collecting new trajectories with the current policy and feeding them back into later rounds.
arXiv Machine Learning
Jun 5
Catastrophic Forgetting as Accessibility Collapse: A Three-Level Framework for Knowledge Persistence in Continual Learning
arXiv:2606. 06032v1 Announce Type: new Abstract: Catastrophic forgetting is commonly interpreted as the irreversible erasure of previously acquired knowledge during sequential learning.
arXiv Machine Learning
Jul 30
The Art of Not Forgetting A Local Learning Architecture for Continual Learning
arXiv:2607. 26523v1 Announce Type: new Abstract: We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?
arXiv AI
Sep 1
On the Plasticity Collapse in Continual Machine Unlearning
The paper investigates continual machine unlearning, where models must forget data over time. It identifies a fundamental issue called plasticity collapse, where successive unlearning requests cause geometric constraints that saturate parameter space, leading to two failure modes: forward failure (reduced forgetting quality) and backward failure (re‑memorization). Experiments across architectures and datasets confirm that plasticity collapse is a pervasive problem in continual unlearning.
arXiv AI
Sep 10
CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion
CUNO is a curriculum‑based graph unlearning framework that progressively removes a designated set of training samples, ordering them by estimated unlearning difficulty across multiple stages. It also introduces a distribution‑level negative preference optimization objective at each stage to steer the model away from its original behavior on the current forget subset while preserving performance on retained data. Experiments show that CUNO mitigates catastrophic unlearning, retaining 74% of original utility at 20% deletion and more than half at 50% deletion, outperforming existing methods.
Hugging Face Trending Papers
Jul 29
The Art of Not Forgetting A Local Learning Architecture for Continual Learning
We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre? sents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns through local updates without end-to-end backpropagation through its feature-generating system.
arXiv AI
Jun 10
Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
arXiv:2606. 10338v1 Announce Type: cross Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored.
arXiv AI
Aug 5
Taming the Implicit: Dual-Channel Risk-Aware Reinforcement Fine-Tuning for Continual Multimodal Post-Training
arXiv:2608. 03660v1 Announce Type: new Abstract: Reinforcement fine-tuning (RFT) is widely believed to inherently resist catastrophic forgetting in continual post-training of multimodal large language models.
arXiv Machine Learning
Jun 30
The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning
arXiv:2606. 29832v1 Announce Type: new Abstract: Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy.
arXiv Machine Learning
2d ago
ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning
arXiv:2609.38833v1 Announce Type: new Abstract: Federated continual learning must integrate new tasks over time without losing earlier-task knowledge. Most existing methods attach an anti-forgetting...