arXiv Machine Learning
Jul 22
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
arXiv:2607. 18639v1 Announce Type: new Abstract: Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.
CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence
arXiv:2606. 27683v1 Announce Type: cross Abstract: Edge devices increasingly invoke large language models (LLMs) through API services for context aware edge intelligence, while edge generated data may be collected to improve LLMs and may introduce sensitive, copyrighted, harmful, or outdated information into model behavior.
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arXiv Machine Learning
Jun 5
Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance
arXiv:2606. 06320v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities.
Hugging Face Trending Papers
Jul 21
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e. g.
arXiv AI
Aug 5
Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation
arXiv:2608. 03791v1 Announce Type: new Abstract: Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora.
arXiv Machine Learning
Jun 5
SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
arXiv:2605. 07482v2 Announce Type: replace Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full retraining.
arXiv Machine Learning
Jul 15
Inference-Time Machine Unlearning via Gated Activation Redirection
arXiv:2605. 12765v3 Announce Type: replace Abstract: Large Language Models memorize vast amounts of training data, raising concerns regarding privacy, copyright infringement, and safety.
Hugging Face Trending Papers
Aug 4
Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation
Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems.
arXiv AI
Jun 2
Visual-Noise Guided In-Context Distillation for Multimodal Large Language Model Unlearning
arXiv:2606. 00105v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on vision-language tasks, but they may also memorize and expose sensitive or restricted knowledge, raising concerns about privacy and broader safety risks.
arXiv AI
1d ago
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
arXiv:2608. 16700v1 Announce Type: cross Abstract: Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data $D_f$ removed from a machine learning model.
arXiv Machine Learning
Jul 1
Probing Memorization of Tabular In-Context Learning
arXiv:2606. 31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.
arXiv Machine Learning
Jul 22
Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning
arXiv:2607. 18615v1 Announce Type: cross Abstract: Machine unlearning for vision-language models (VLMs) remains underexplored.
arXiv AI
Jun 16
Control-Plane Placement Shapes Forgetting: An Architectural Study of Agent Memory Across Thirteen System Configurations
arXiv:2606. 15903v1 Announce Type: cross Abstract: Where an LLM sits in an agent memory pipeline -- between the recall plane that retrieves stored facts (extensively benchmarked) and the control plane that mutates them via supersede, release, purge (largely untested) -- shapes which forgetting failure modes the system recovers.