Continual Learning papers, 2023–2026

Key findings

• Continual Learning went from 77 papers in 2023 to 131 in 2026, roughly 1.7x in three years.
• NeurIPS publishes the most Continual Learning work: 121 papers from 2023 to 2026, 32% of the four-venue total.
• The fastest-growing overlap is with Low-Rank Adaptation: papers tagged with both rose from 1 in 2023 to 16 in 2026.
• Among the 5 topics under Meta-Learning and Transfer, it ranks #5 by 2026 paper count; the fastest grower in the group, Foundation Model Adaptation, is up 3.8x versus 1.7x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Bayes-pFCL:Bayesian Personalized Federated Continual Learning
• ICML 2026Beyond Buffer Limits: Energy-Based Data Reassembly for Continual Learning
• ICLR 2026FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning
• CVPR 2026A Faster Path to Continual Learning
• NeurIPS 2025AnaCP: Toward Upper-Bound Continual Learning via Analytic Contrastive Projection
• ICML 2025BECAME: Bayesian Continual Learning with Adaptive Model Merging
• ICLR 2025Active Learning for Continual Learning: Keeping the Past Alive in the Present
• CVPR 2025AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering
• NeurIPS 2024CoIN: A Benchmark of Continual Instruction Tuning for Multimodel Large Language Models
• ICML 2024An Effective Dynamic Gradient Calibration Method for Continual Learning
• ICLR 2024A Probabilistic Framework for Modular Continual Learning
• CVPR 2024Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay
• NeurIPS 2023Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
• ICML 2023Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation
• ICLR 2023Artificial Neuronal Ensembles with Learned Context Dependent Gating
• CVPR 2023Achieving a Better Stability-Plasticity Trade-Off via Auxiliary Networks in Continual Learning
• NeurIPS 2026Beyond Exemplar Selection: Value-Aware Memory Allocation in Replay-Based Continual Learning
• ICML 2026BTSP-CAM: A Brain-Inspired Geometric Memory for Class-Incremental Learning
• ICLR 2026IDER: IDEMPOTENT EXPERIENCE REPLAY FOR RELIABLE CONTINUAL LEARNING
• CVPR 2026AdaPrior Bayesian-Inspired Adaptive Prior Correction for Long-Tailed Continual Learning

All Continual Learning papers

39 papers, page 1 of 1

1. NeurIPS 2026A Theoretical Bridge Between Long-Tailed Recognition and Continual Learning
2. NeurIPS 2026Anchoring Physiological Invariance, Expanding Domain Plasticity: Prior-Stabilized Dynamic Adaptation for Continual rPPG Measurement
3. NeurIPS 2026Bayes-pFCL:Bayesian Personalized Federated Continual Learning
4. NeurIPS 2026Beyond Exemplar Selection: Value-Aware Memory Allocation in Replay-Based Continual Learning
5. NeurIPS 2026Combating Catastrophic Forgetting in Continual Domain Adaptation via Knowledge Recasting
6. NeurIPS 2026Concentrated Gradients Amplify Forgetting: Dominant-direction Projection for Continual Multimodal Learning
7. NeurIPS 2026Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models
8. NeurIPS 2026Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning
9. NeurIPS 2026Distance-Dependent Connectivity Shapes Continual Learning by Synaptic-Resource-Delimited Separation of Neural Dynamics
10. NeurIPS 2026Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
11. NeurIPS 2026FedSOUL: Federated Continual Unlearning via Spectral Orthogonality
12. NeurIPS 2026FedVaccine: Knowledge Recall after Spatial-Temporal Catastrophic Forgetting in Federated Continual Learning via Gradient-Based Vaccine
13. NeurIPS 2026FOGO: Forgetting-aware Orthogonalization Optimizer
14. NeurIPS 2026Forgetting Has Neighbors: Localized Collateral Forgetting in Machine Unlearning
15. NeurIPS 2026Forgetting to Improve: Principled Data Removal in Active Learning
16. NeurIPS 2026From Anti-Forgetting to Fast Adaptation: Continual Reinforcement Learning with World Models
17. NeurIPS 2026From Generic to Dedicated: A Novel Optimizer for Online Continual Learning
18. NeurIPS 2026HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
19. NeurIPS 2026Heterogeneity-aware Distillation for Federated Continual Learning
20. NeurIPS 2026Hidden Forgetting in Continual Multimodal Learning: When Accuracy Survives but Grounding Fails
21. NeurIPS 2026Improving Continual Video Instance Segmentation via Spatial-Temporal Balanced Mixture-of-Experts Adapters
22. NeurIPS 2026InTAct: Interval-based Task Activation Consolidation for Continual Learning
23. NeurIPS 2026Lost or Hidden? A Concept-Level Forgetting in Supervised Continual Learning
24. NeurIPS 2026Mitigating Asymmetric Boundary Encroachment in Continual Learning of Vision-Language Models
25. NeurIPS 2026Modulating Merging Strengths via Joint Loss Estimation for LoRA-based Continual Learning
26. NeurIPS 2026MoRe: Modular Representations for Principled Continual Representation Learning on Sequential Data
27. NeurIPS 2026NADS: Navigator-Guided Data Selection for Mitigating Catastrophic Forgetting in Fine-Tuning
28. NeurIPS 2026Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
29. NeurIPS 2026PM-LoRA: Scalable Continual Learning via Progressive Merging of Low Rank Adapters
30. NeurIPS 2026Spectral Rank Calibration for Continual LoRA Merging in Multimodal Large Language Models
31. NeurIPS 2026STABLE: A Continual Learning Optimizer with Adaptive Drift Control
32. NeurIPS 2026Subspace-Guided Continual Learning: Hessian Based Stable–Plastic Decomposition for Exemplar-Free Class-Incremental Learning
33. NeurIPS 2026Towards Better Generalization in Lifelong Person Re-Identification with Flatness-Aware Learning
34. NeurIPS 2026Trust Region Continual Learning as an Implicit Meta-Learner
35. NeurIPS 2026Two Drifts, One Principle: Conflict-Aware Spectral Consolidation for Multimodal Continual Learning
36. NeurIPS 2026UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
37. NeurIPS 2026Unlearning Diffusion Policies via Relative Fisher Forgetting
38. NeurIPS 2026Unlocking Compositional Generalization in Continual Few-Shot Learning
39. NeurIPS 2026When Catastrophic Inheritance Meets Forgetting in Continual Adaptation of Foundation Models

FAQ

How many Continual Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

381 in total: 77 in 2023, 69 in 2024, 104 in 2025, 131 in 2026.

How many Continual Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 39 Continual Learning papers, versus 32 the year before.

How many Continual Learning papers are at ICLR 2026?

ICLR 2026 has 28 Continual Learning papers, versus 23 the year before.

How many Continual Learning papers are at ICML 2026?

ICML 2026 has 42 Continual Learning papers, versus 29 the year before.

How many Continual Learning papers are at CVPR 2026?

CVPR 2026 has 22 Continual Learning papers, versus 20 the year before.

Which conference publishes the most Continual Learning papers?

NeurIPS, with 121 papers from 2023 to 2026.