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Interference-Aware Multi-Task Unlearning

BACKGROUND
11 SIGNALSFIRST DETECTED 20 May 2026
Narrative synthesis pending — 11 signals collected.
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Interference-Aware Multi-Task Unlearning
REPORT · WHAT · ArXiv CS.AI
6.3
Other feeds and industry voices
28 Sept
Neuralyzing the Trace: Selective Representation-Level Unlearning with Contrastive Sparse Autoencoders

arXiv:2609.31056v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted information while preserving a model's other abilities. In realistic settings, such as

REPORT · WHAT · ArXiv CS.AIOPEN STORY
15 Oct
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning

arXiv:2610.14605v1 Announce Type: new Abstract: Current evaluations of multimodal unlearning typically focus on whether a model stops outputting target information. This

REPORT · WHAT · ArXiv CS.AIOPEN STORY
29 Jun
Position: The Term "Machine Unlearning" Is Overused in LLMs

arXiv:2606.27379v1 Announce Type: cross Abstract: Large language models increasingly face demands to "forget" training data, knowledge, or behaviors due to regulatory deletion

REPORT · WHAT · ArXiv CS.AIOPEN STORY
27 Apr
PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training

arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining

REPORT · WHAT · ArXiv CS.AIOPEN STORY
20 Mar
A Concept is More Than a Word: Diversified Unlearning in Text-to-Image Diffusion Models

arXiv:2603.18767v1 Announce Type: new Abstract: Concept unlearning has emerged as a promising direction for reducing the risks of harmful content generation in text-to-image

REPORT · WHAT · ArXiv CS.AIOPEN STORY
8 Apr
PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models

arXiv:2604.05634v1 Announce Type: new Abstract: Machine unlearning (MU) has become a critical technique for GenAI models' safe and compliant operation. While existing MU methods

REPORT · WHAT · ArXiv CS.AIOPEN STORY
9 Apr
CURE:Circuit-Aware Unlearning for LLM-based Recommendation

arXiv:2604.04982v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have opened new opportunities for recommender systems by enabling rich semantic

REPORT · WHAT · ArXiv CS.AIOPEN STORY
2 Sept
I-CARE: Analysis of interference-related phenomena in a controllable, diverse and representative unlearning setting for text-to-image models

arXiv:2609.00003v1 Announce Type: new Abstract: Machine unlearning studies the removal of knowledge from an AI model, making the system forget a concept it previously learned.

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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