Continual Learning

Follow this topic Ask about this topic

Continual Learning studies how models adapt to sequences of tasks or non-stationary data without losing useful earlier knowledge, a challenge known as catastrophic forgetting. Common approaches replay selected examples, regularize updates to protect important parameters, isolate tasks with adapters or separate subnetworks, or manage knowledge through prompts, memory, and model expansion. Methods are tested on class- and domain-incremental image benchmarks such as CIFAR and ImageNet, as well as task-free streams and applications in language, robotics, and healthcare. A central tension is balancing retention with the capacity to learn, while evaluations increasingly question assumptions such as known task boundaries and seek to measure transfer, recovery, resource use, and performance under realistic shifts.

Latest papers

304 papers35 papers in the last four weeks, up 84% on the four weeks before. 0.2% of all new papers.

Sequential Learning and Catastrophic Forgetting in Differentiable Resistor Networks
May 2, 2026Maniru IbrahimContinual Learning Physical Neural Networks
Mathematics Applications Consortium for Science and Industry (MACSI), Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland
Autonomous Drift Learning in Data Streams: A Unified Perspective
May 2, 2026Xiaoyu Yang, En Yu, Jie LuConcept Drift Continual Learning
Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
Continual Learning of Feedback-based Molecular Communication
May 1, 2026Siddhant Setia, Junichi Suzuki, Tadashi NakanoContinual Learning Replay-Based Continual Learning
Department of Computer Science, University of Massachusetts, Boston Boston, MA, 02125, USA · Graduate School of Informatics, Osaka Metropolitan University Osaka, Japan
Contextual Agentic Memory is a Memo, Not True Memory
Apr 30, 2026Binyan Xu, Xilin Dai, Kehuan ZhangAgent Memory Continual Learning for LLM Agents
The Chinese University of Hong Kong, Hong Kong, China · Zhejiang University, Hangzhou, China
When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry
Apr 30, 2026Kathrin Korte, Joachim Winter Pedersen, Eleni Nisioti +1Intrinsic Dimensionality Continual Learning
IT University of Copenhagen, Denmark
Learning to Forget: Continual Learning with Adaptive Weight Decay
Apr 29, 2026Aditya A. Ramesh, Alex Lewandowski, Jürgen SchmidhuberDeep Learning Optimization Weight Decay
The Swiss AI Lab, IDSIA USI-SUPSI, Lugano, Switzerland · University of Alberta, Alberta Machine Intelligence Institute, Edmonton, Canada · Center of Excellence for Generative AI, KAUST, Thuwal, Saudi Arabia
NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning
Apr 29, 2026Karthik Charan Raghunathan, Christian Metzner, Laura Kriener +1Stability-Plasticity Dilemma Dynamic Neural Networks
Institute of Neuroinformatics University of Zurich & ETH Zurich Zurich, Switzerland
TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Apr 28, 2026Dominik Żurek, Kamil Faber, Marcin Pietron +2Continual Learning Robotic RL
AGH University of Krakow, Poland · American University, Washington, DC, USA
Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks
Apr 27, 2026Kevin McKee, Thomas Hazy, Yicong Zheng +2Continual Learning Incremental Learning
Astera Institute
Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability
Apr 23, 2026Nicolae Filat, Ahmed Hussain, Konstantinos Kalogiannis +1Continual Learning
Bitdefender, Romania · KTH Royal Institute of Technology · Stockholm, Sweden +2
Same Methods, Different Rankings: Trainable Depth as an Evaluation Variable in Continual Learning
Apr 23, 2026Paul-Tiberiu Iordache, Elena Burceanu, Mihai DascaluTask-Incremental Learning Fine-Tuning
Bitdefender, Romania · Politehnica University of Bucharest, Romania
ImageHD: Energy-Efficient On-Device Continual Learning of Visual Representations via Hyperdimensional Computing
Apr 23, 2026Jebacyril Arockiaraj, Dhruv Parikh, Viktor PrasannaVisual Representation Learning Energy-Efficient ML
University of Southern California, Los Angeles, CA, USA
Task Switching Without Forgetting via Proximal Decoupling
Apr 20, 2026Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +2Proximal Optimization Continual Learning
Department of Computer Science, University of York, UK · SEIEE, Shanghai Jiao Tong University, China · LIVIA, Dept. of Systems Engineering, ETS Montreal, Canada +1
Incremental learning for audio classification with Hebbian Deep Neural Networks
Apr 20, 2026Riccardo Casciotti, Francesco De Santis, Alberto Antonietti +1Hebbian Learning Continual Learning
Signal Processing Research Centre, Tampere University, Tampere, Finland · Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Italy
IncreFA: Breaking the Static Wall of Generative Model Attribution
Apr 20, 2026Haotian Qin, Dongliang Chang, Yueying Gao +3Continual Learning Open-Set Recognition
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China · Tsinghua University, China
Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
Apr 19, 2026Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu +2Continual Learning Replay-Based Continual Learning
University of Pennsylvania
Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains
Apr 18, 2026Dongkyu Cho, Xiyue Li, Samrachana Adhikari +1Concept Bottleneck Models Healthcare
Computer Science Department New York University New York, NY, USA · Department of Population Health NYU Grossman School of Medicine New York, NY, USA · School of Global Public Health Computer Science Department New York University New York, NY, USA
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
Apr 16, 2026Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1Class-Incremental Learning Concept Bottleneck Models
Department of Electrical and Computer Engineering, University of California San Diego · Department of Computer Science and Engineering, University of California San Diego · Halıcıoğlu Data Science Institute, University of California San Diego
Mistake gating leads to energy and memory efficient continual learning
Apr 15, 2026Aaron Pache, Mark CW van RossumEnergy-Efficient ML Continual Learning
School of Mathematical Sciences University of Nottingham, Nottingham NG7 2RD, United Kingdom · School of Psychology University of Nottingham, Nottingham NG7 2RD, United Kingdom
Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay
Apr 15, 2026Qianyu Chen, Shujian YuContinual Learning Replay-Based Continual Learning
†Nanyang Technological University · ‡VU Amsterdam · §UiT The Arctic University of Norway
ReConText3D: Replay-based Continual Text-to-3D Generation
Apr 15, 2026Muhammad Ahmed Ullah Khan, Muhammad Haris Bin Amir, Didier Stricker +1Text-to-3D Generation 3D Asset Generation
DFKI · RPTU Kaiserslautern-Landau
From Order to Distribution: An Exact Operator Framework for Forgetting in Continual Learning
Apr 15, 2026Zonghuan Xu, Xingjun MaContinual Learning Catastrophic Forgetting
Institute of Trustworthy Embodied AI, Fudan University, Shanghai, China · Shanghai Key Laboratory of Multimodal Embodied AI, Shanghai, China
BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning
Apr 14, 2026Jagadeesh Rachapudi, Ritali Vatsi, Praful Hambarde +1Continual Learning Machine Unlearning
Indian Institute of Technology Mandi, Mandi, India
HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning
Apr 13, 2026Yangfan Wang, Tianyang Sun, Chen Tang +3Model Editing Language Model Editing
Harbin Institute of Technology · AI Research Center, Midea Group (Shanghai) Co., Ltd. · State Key Laboratory of Smart Farm Technologies and Systems +1
A Faster Path to Continual Learning
Apr 13, 2026Wei Li, Hangjie Yuan, Zixiang Zhao +3Continual Learning Neural Network Optimization
College of Computer Science, Sichuan University, China · College of Computer Science and Technology, Zhejiang University, China · Photogrammetry and Remote Sensing Lab, ETH Zürich, Switzerland +3
Information as Structural Alignment: A Dynamical Theory of Continual Learning
Apr 8, 2026Radu NegulescuContinual Learning Catastrophic Forgetting
The Informational Buildup Foundation
Critical Patch-Aware Sparse Prompting with Decoupled Training for Continual Learning on the Edge
Apr 8, 2026Wonseon Lim, Jaesung Lee, Dae-Won KimContinual Learning On-Device Training
School of Computer Science and Engineering, Chung-Ang University · Department of Artificial Intelligence, Chung-Ang University
Is Prompt Selection Necessary for Task-Free Online Continual Learning?
Apr 6, 2026Seoyoung Park, Haemin Lee, Hankook LeeTask-Free Continual Learning Continual Learning
Sungkyunkwan University
Non-Equilibrium Stochastic Dynamics as a Unified Framework for Insight and Repetitive Learning: A Kramers Escape Approach to Continual Learning
Apr 5, 2026Gunn KimContinual Learning Stability-Plasticity Dilemma
Department of Physics, Sejong University, Seoul 05006, Republic of Korea
When Modalities Remember: Continual Learning for Multimodal Knowledge Graphs
Apr 3, 2026Linyu Li, Zhi Jin, Yichi Zhang +5Multimodal KGs Continual Learning
Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education School of Computer Science, Peking University Beijing, China · School of Computer Science Zhejiang University Hangzhou, China · School of Cyber Science and Engineering Wuhan University Wuhan, China +1

May 2, 2026J/K move · Enter open · S save

Sequential Learning and Catastrophic Forgetting in Differentiable Resistor Networks

Maniru Ibrahim

Mathematics Applications Consortium for Science and Industry (MACSI), Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland

Continual Learning Physical Neural Networks Catastrophic Forgetting

Open paper AskPDF arXiv

Differentiable physical networks provide a simple setting in which learning can be studied through the interaction between trainable parameters and physical equilibrium constraints. We investigate sequential learning in differentiable resistor networks governed by Kirchhoff's laws. Although individual input--output mappings can be learned by gradient-based adjustment of edge conductances, sequential training on conflicting tasks produces catastrophic forgetting. We show that forgetting is controlled by task conflict and by the degree of adaptation to the new task. Uniform anchoring and normalised gradient-weighted anchoring reduce forgetting only by increasing the final loss on the new task, giving a clear forgetting--adaptation trade-off. We also show that forgetting is associated with localised conductance changes on high-current edges, giving a physical interpretation as reconfiguration of dominant transport pathways. Broader random-task ensembles show that the strongest forgetting occurs when the second task reverses the output ordering imposed by the first task. Finally, comparisons across Erdős--Rényi, small-world, scale-free, and random-geometric graph ensembles show that topology changes the forgetting--adaptation balance. These results position differentiable resistor networks as compact, physically interpretable testbeds for studying continual learning in tunable matter.