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?
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
Jun 4
\textsc{Lethe}: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
arXiv:2601. 22601v3 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase knowledge from a global model.
arXiv AI
Jun 3
Forgetting is Not Erasure: Recovering Latent Knowledge via Transport Keys
arXiv:2606. 02860v1 Announce Type: cross Abstract: Catastrophic forgetting is often framed as a representational problem: after sequential training, a model appears to lose the features that supported performance on earlier tasks.
Hugging Face Trending Papers
Jun 29
The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning
Machine unlearning aims to eliminate the influence of specific data from trained models to safeguard privacy. However, this presents a significant challenge in the context of continual learning (CL), where models update sequentially on dynamic datasets.