Continual Learning

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Period ending 2026-09-21

14 new papers

A weekly snapshot of new work published in Continual Learning.

Period ending 2026-09-14

11 new papers

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Period ending 2026-09-07

8 new papers

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Continual Learning Methods

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229 papersExplore

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Efficient Continual Learning

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Catastrophic Forgetting

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151 papersExplore

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Forgetting

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Plasticity

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81 papersExplore

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Domain Incremental Learning

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62 papersExplore

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Retention

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51 papersExplore

621 papers

Latest in Continual Learning

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LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Sep 3, 2025Daniela Gottesman, Alon Gilae-Dotan, Ido Cohen +4Large Language Model Pretraining Multimodal Knowledge Editing
The Bayesian Approach to Continual Learning: An Overview
Jul 11, 2025Tameem AdelEfficient Continual Learning Continual Learning Methods
Efficient Unlearning with Privacy Guarantees
Jul 7, 2025Josep Domingo-Ferrer, Najeeb Jebreel, David SánchezSeveral Approximate Unlearning Strategies AI Privacy Risks and Protection
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Jun 12, 2025Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2Efficient Continual Learning Low-Rank Adaptation
SEAL: Searching Expandable Architectures for Incremental Learning
May 15, 2025Matteo Gambella, Manuel RoveriDomain Incremental Learning Neural Architecture Search
Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
May 2, 2025Zhiqiang He, Zhi LiuPlasticity Non-Stationarity
Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection
Apr 14, 2025Songze Li, Qixing Xu, Tonghua Su +3Plasticity Model Pretraining
Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models
Apr 14, 2025Yujing Wang, Jinwen Chen, Hainan Zhang +5Concept Erasure Anonymization
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Mar 19, 2025Can Peng, Qianhui Men, Pramit Saha +5Federated Learning Efficient Continual Learning
Attention is All You Need Until You Need Retention
Jan 15, 2025M. Murat YasliogluRetention Generative Pretrained Transformers
Collaborative Knowledge Distillation via a Learning-by-Education Node Community
Sep 30, 2024Anestis Kaimakamidis, Ioannis Mademlis, Ioannis PitasKnowledge Distillation Chain-Of-Thought Distillation
TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection
Sep 24, 2024Francesco Pasti, Riccardo De Monte, Davide Dalle Pezze +2Robotic Datasets Efficient Continual Learning
Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion
Aug 19, 2024Achref Jaziri, Etienne Künzel, Visvanathan RameshEfficient Continual Learning Continual Learning Methods
Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning
Jul 8, 2024Xiaojie Li, Yibo Yang, Jianlong Wu +4Domain Incremental Learning Few-Shot Learning
Realistic Continual Learning Approach using Pre-trained Models
Apr 11, 2024Nadia Nasri, Carlos Gutiérrez-Álvarez, Sergio Lafuente-Arroyo +2Efficient Continual Learning Continual Learning Methods
AppAgent: Multimodal Agents as Smartphone Users
Dec 21, 2023Chi Zhang, Zhao Yang, Jiaxuan Liu +6Multimodal Agents Device
DIRA-SS:Dynamic Domain Incremental Regularised Adaptation -- Self-Supervised
Nov 13, 2023Abanoub Ghobrial, Kerstin EderDomain Adaptation Self-Supervised
The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
Date pendingAaryaman Kalani, Murari Mandal, Dhruv Kumar +2Machine Unlearning Batch Normalization
CARRE: Counterfactual Action Retrieval and Reason Evaluation for Explainable Churn Prescription
Date pendingMinjoo Kim, Sangjin Park, Seung Hwan ChoChurn Prediction Counterfactual Reasoning
EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?
Date pendingZixuan Ke, Vaidehi Patil, Haizhou Shi +9Agent Harness Llm-Based Agent
DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
Date pendingBingchen Huang, Yifu Chen, Zhiling Wang +1Domain Incremental Learning Self-Supervised Vision Transformers

Sep 3, 2025 · cs.CLJ/K move · Enter open · S save

LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations

Daniela Gottesman, Alon Gilae-Dotan, Ido Cohen, Yoav Gur-Arieh+3

Tel Aviv University · Mila – Quebec AI Institute & McGill University

Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models turn data into representations of knowledge and beliefs about the world are poorly understood. To facilitate such studies, we present LMEnt, a suite including (1) a knowledge-rich pretraining corpus, fully annotated with entity mentions based on Wikipedia, (2) an entity-based retrieval method over pretraining data that outperforms existing tools by as much as 80.4%, and (3) 12 pretrained LMs with up to 1B parameters and 4K intermediate checkpoints, with comparable performance to popular open-source models on knowledge tasks. Together, these resources provide a controlled environment for analyzing connections between entity mentions in pretraining data and downstream performance. We show the utility of LMEnt by studying knowledge acquisition over training, finding that entity co-occurrence and mention forms-which are difficult to study with existing tools-affect learning trends. Moreover, as LMs form stronger associations between entities, their facts are harder to edit in-context, whereas inconsistencies in model predictions over training are indicative of editing success. We release LMEnt to support studies of knowledge in LMs, including knowledge representations, plasticity, editing, attribution, hallucinations, and learning dynamics.

Large Language Model Pretraining Multimodal Knowledge Editing Co-Occurrence Ear Plasticity

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