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

A weekly snapshot of new work published in Continual Learning.

Period ending 2026-09-07

8 new papers

A weekly snapshot of new work published in Continual Learning.

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Continual Learning Methods

Within Continual Learning

229 papersExplore

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Efficient Continual Learning

Within Continual Learning

194 papersExplore

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Catastrophic Forgetting

Within Continual Learning

151 papersExplore

Focused topic

Forgetting

Within Continual Learning

105 papersExplore

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Plasticity

Within Continual Learning

81 papersExplore

Focused topic

Domain Incremental Learning

Within Continual Learning

62 papersExplore

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Retention

Within Continual Learning

51 papersExplore

620 papers

Latest in Continual Learning

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A Lightweight Plastic-Memory Framework for Graph Few-Shot Class-Incremental Learning
Sep 22, 2026Zihan Mei, Zhili Qin, Tongze Zhang +3Domain Incremental Learning
Muon Can Outperform Dedicated Continual Learning Methods
Sep 21, 2026Sebastian George Sincari, Bogdan Alexandru Gheorghe, Antonio BarbalauEfficient Continual Learning
Brain-Inspired Hierarchical Modularity for General Continual Learning
Sep 21, 2026Hongwei Yan, Kanglei Zhou, Qi Cheng +7Efficient Continual Learning Experience-Driven Learning
Stable Unsupervised Continual Chunking with Sheaf SyncMap
Sep 21, 2026Xueyuan Li, Danilo Vasconcellos VargasEfficient Continual Learning Unsupervised
CE $^4$ L: Continual Ego, Exo, and Ego-Exo LearningCE^4L: Continual Ego, Exo, and Ego-Exo Learning
Sep 20, 2026Hongwei Yan, Kanglei Zhou, Yuchen Liu +3Efficient Continual Learning Multi--Task Learning
Past, Future, All at Once: Mitigating Stability-Plasticity Dilemma via Post-hoc JANUS Rectification
Sep 17, 2026Zhilong Zheng, Letian Tao, Yang Guan +7Catastrophic Forgetting Plasticity
An Architecture for Long-Horizon Agents: Levels, Ticks and Cascaded Intelligence
Sep 17, 2026Erik Nijkamp, Anurag Koul, Egor Pakhomov +1Long-Horizon Agents Agent Memory
MCLC-NET: Multimodal Continual Learning for Leaf Counting
Sep 16, 2026Ruchi Bhatt, Pratibha Kumari, Shreya Bansal +3Apple Tree Leaf Disease Segmentation Dataset Crop
Uncertainty-Aware Continual Learning for Open-World Intent Discovery Under an evolving Label Space
Sep 15, 2026Pisante Aida, Formentin SimoneEfficient Continual Learning Dark Knowledge
Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation
Sep 15, 2026Hojin Lee, Yunho Lee, Daniel A Duecker +1Traversability Efficient Continual Learning
CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework
Sep 15, 2026Yunxiang Fu, Meng Lou, Zicheng Liao +1Efficient Continual Learning Domain Incremental Learning
Smarter by the Moment: Environment-Driven Dynamic Policies for Continual LLM Improvement
Sep 15, 2026Ting-Wei Chang, Po-Chun Chen, Hen-Hsen Huang +1Efficient Continual Learning
Continuous-Time Machine Learning: A Unified Mathematical Perspective
Sep 15, 2026Waleed Razzaq, Yun-Sheng Zhao, Yun-Bo ZhaoMachine Learning Efficient Continual Learning
What Does Layer-Importance Reveal About Transformers and State-Space Models?
Sep 15, 2026Istabrak Abbes, Nizar Islah, Irina Rish +1Transformer Architectures Transformer Residual Streams
Test-Time Unlearning via Sparse Autoencoder
Sep 14, 2026Pingzhi Li, Jinhao Duan, Vaishnav Tadiparthi +6Language Model Evasion Attacks Forgetting
Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning
Sep 14, 2026Yuxuan Sun, Yuxuan Bai, Tan Chen +2Efficient Continual Learning Heterogeneous Edge Devices
LifeMem: Enabling Lifelong Experience Reuse for LLM Agents
Sep 14, 2026Yuli Qiu, Yutong Li, Wei Su +5Agent Memory Large Language Model Agents
LifeFuse-Mem: Lifecycle-Aware State Fusion Against Temporary Overwriting for Long-Term Memory
Sep 14, 2026Hanyu Zhao, Yuqian Feng, Zhenyu Song +4Agent Memory Long-Term Memory
SCORE: SubDistribution-aware Collaborative Knowledge Reinforcing for Cloth-Hybrid Lifelong Person Re-Identification
Sep 14, 2026Kunlun Xu, Liangyu Ma, Jiangmeng Li +4Re-Identification Diversity
When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents
Sep 12, 2026Qinzhen Ma, Ruihai WuAdmissibility Efficient Continual Learning
When Synthetic Data Hurts: On Catastrophic Forgetting in Skill Retrieval for LLM Agents
Sep 12, 2026Syed Shariyar Murtaza, Yifan Nie, Utkarsh Soni +2Catastrophic Forgetting Synthetic Data
Investigating catastrophic forgetting in sound event classification
Sep 12, 2026Riccardo Casciotti, Annamaria MesarosDomain Incremental Learning Catastrophic Forgetting
Domain-Incremental Learning for Multi-Channel Replay Speech Detection
Sep 11, 2026Michael NeriSpeaker Recognition Domain Incremental Learning
A Bellman Optimality Equation for Plasticity
Sep 11, 2026Jeremy Lucas, Doina PrecupPlasticity Bellman Equation
Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs
Sep 9, 2026Ravi Ranjan, Olivera Kotevska, Agoritsa PolyzouLarge Language Model Unlearning Several Approximate Unlearning Strategies
What Should an Agent Forget? Separating What Is Stored from What Is Used
Sep 9, 2026Yuhang Li, Yuchen LiMultilingual Agents Agentic Retrieval
Learning Length-Extrapolatable Recurrent Models
Sep 8, 2026Hanwen JiangRecurrent Model Catastrophic Forgetting
What Eviction Destroys: A Restore-Counterfactual Audit of Forgetting in Agent Memory
Sep 8, 2026Chen ShenEviction Agent Memory
Popular Knowledge Propagates More Errors in LLM Knowledge Updating
Sep 8, 2026Yuji Zhang, Weibing Wang, Cheng Qian +5Facts Forgetting
Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging
Sep 7, 2026Nazish Khalid, Tausifa Jan Saleem, Amal Saqib +2Medical Imaging Datasets Catastrophic Forgetting
TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning
Sep 7, 2026Avik Bhatnagar, Federico Nicolas Peccia, Oliver BringmannBatch Size Learning Rates
Constitutive State-Space Modeling of Path-Dependent Plasticity: A Resolution-Consistent and Parallelizable Computational Framework
Sep 7, 2026Rui Barreira, Taylan Soydan, Francesco Scipione +2State Space Models State Space
DODR: Deterministic Operator-Driven Reasoning in Latent Space
Sep 7, 2026Weicai Huang (Beijing MQPat Technologies, Co., Ltd.)Efficient Latent Reasoning Inference Engine
Beyond Endpoint Scores: Time- and Capacity-Conditioned Evaluation of Continual Knowledge Updating
Sep 3, 2026Heejin ChoiUpdates Task-Aware Evaluation Framework
Neural-Collapse-guided Task-Free Continual Anomaly Detection
Sep 3, 2026Xiaotong Kong, Chaoyang Song, Ziai Zhou +3Zero-Shot Anomaly Detection Anomaly Detection
Confess What You Know: Forget-Set Misalignment with Model Knowledge in LLM Unlearning
Sep 1, 2026Miso Kim, Georu Lee, Seungwon Jeong +1Large Language Model Unlearning Forgetting
Hypotheses-Guided Self Distillation for Continual Personalization
Aug 31, 2026EunJeong Hwang, Kushan Mitra, Dan Zhang +2Personalization Efficient Continual Learning
One Adapter, Many Tasks: Task-Conditioned Feature Transformations for Continual Learning
Aug 31, 2026Yunxiang Fu, Meng Lou, Yizhou YuContinual Learning Methods
Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents
Aug 31, 2026Le Chen, Zishen Wan, Baixi Sun +6Working Memory Coding Agents
Where Identity Lives: Localized, Retain-Free Identity Unlearning in Multimodal Large Language Models
Aug 31, 2026Kangwook Ko, Jaehyuk Jang, Wonjun Lee +2Several Approximate Unlearning Strategies Unlearning Aims
Kathleen Remembers: Length-Invariant One-Shot Recall Without Attention
Aug 31, 2026George FountzoulasFactual Recall Memory Retrieval
Knowing Beyond the Known: Reinforced Knowledge Specification for Multi-Label Class-Incremental Learning
Aug 31, 2026Aoting Zhang, Dongbao Yang, Chang Liu +3Domain Incremental Learning Catastrophic Forgetting
SELECT: SELEctive Context Transfer for Class-Incremental Semantic Segmentation
Aug 31, 2026Avi Gupta, Saurabh Yadav, Koteswar Rao Jerripothula +1Domain Incremental Learning Plasticity
CPR for LLMs: Critical-Point Routing against Catastrophic Forgetting in Domain Adaptation
Aug 31, 2026Kwangmin Ki, Yunhun Nam, Jongheon Jeong +1Large Language Model Adaptation Supervised Fine-Tuning
Forget or Fine-tune? A Comparative Study of Machine Unlearning Strategies for Noisy Label Correction
Aug 30, 2026João L. P. Santana, Filipe R. CordeiroNoisy Labels Machine Unlearning
Mycelial Search: A Graph-Structured Metaheuristic for Continuous Optimisation
Aug 24, 2026Mohammad Mahdi DehshibiMetaheuristics Plasticity
What to Preserve, Where to Adapt: A Depth-Wise Analysis of Forgetting in Continual Gynecological Image Segmentation
Aug 13, 2026Amal Saqib, Tausifa Jan Saleem, Numan Saeed +1Catastrophic Forgetting Medical Image Segmentation
Mind the Context: Continual Learning of Socially Appropriate Robot Actions via Environmental-Social Disentanglement
Aug 13, 2026Rafal Robert Karpinski, Fethiye Irmak Dogan, Nikhil Churamani +4Scalable Robot Learning Robot Systems
Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
Aug 13, 2026Lucia MalíčkováPlasticity
Uniform Herding: Exemplar Replay with Representation Refresh
Aug 13, 2026Krishna SubediRetraining Forgetting
Incremental Evaluation and Training in Relational Deep Learning
Aug 13, 2026Jakub Peleška, Gustav ŠírRelational Deep Learning Efficient Continual Learning
Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning
Aug 13, 2026Zeyang Zhang, Tieliang Gong, Junyan Lu +1Efficient Continual Learning Plasticity
AWARe: Mitigating Catastrophic Forgetting via Activation-Weighted Adaptive REtention
Aug 12, 2026Juncheng Liao, Jinfan Lv, Guoming Wang +3Catastrophic Forgetting Retention
Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning
Aug 12, 2026Tieliang Gong, Zhongbo Zhang, Wen Wen +1Catastrophic Forgetting Continual Learning Methods
Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding
Aug 11, 2026Kushal ChakrabartiCoding Agents Procedural Knowledge
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
Aug 11, 2026Qiang Wang, Songlin Dong, Shaokun Wang +5Domain Incremental Learning Multimodal Domain Generalization
Sequential Modality Dropout for Robust Multi-Modal Sequential Recommendation
Aug 10, 2026Guanqun Yang, Wenlong ZhangMultimodal Recommendation Dropout
Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA
Aug 10, 2026Mind Lab, :, Vin Bo +74Efficient Continual Learning Continual Learning Methods
Hyperbolic Multimodal Continual Learning
Aug 10, 2026Jiahong Liu, Ming Shen, Xiaohao Liu +4Efficient Continual Learning Continual Learning Methods
Agentic Router: An Execution-Grounded Continual Learning Approach With Memory
Aug 10, 2026Yuxuan Chen, Rongpeng Li, Zhifeng Zhao +3Qwen3 Data Generation

Sep 22, 2026 · cs.LGJ/K move · Enter open · S save

A Lightweight Plastic-Memory Framework for Graph Few-Shot Class-Incremental Learning

Zihan Mei, Zhili Qin, Tongze Zhang, Hongyuan Liu+2

Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches primarily address catastrophic forgetting by preserving node-related knowledge through replay or distillation techniques; however, they often incur high computational costs and inefficiency. This issue is further exacerbated in real-world scenarios where labeled data for new classes is scarce. In this paper, we propose a novel lightweight plastic-memory framework specifically designed for few-shot incremental learning on graphs. The core idea of our framework is the construction of a plastic-memory module that evolves over time, continuously updating and expanding its memory to accommodate new classes while retaining previously learned knowledge. In contrast to existing techniques, our memory module is both lightweight and effective, featuring an innovative evolving micro-clustering structure that dynamically updates representations of class prototypes, sub-prototypes, and their interaction weights. Building on this memory module, we introduce a memory-driven meta-learning framework that enhances adaptability to new tasks in its inner loop while maintaining stability for earlier tasks in the outer loop. Extensive experiments on four benchmark datasets demonstrate the framework's superior performance in balancing stability for old knowledge and adaptability to new knowledge.

Domain Incremental Learning

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