arXiv AI
Aug 13
Representation Finetuning for Continual Learning
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework
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arXiv Machine Learning
Sep 15
Realistic Continual Learning Approach using Pre-trained Models
arXiv:2404.07729v2 Announce Type: replace Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forg...
arXiv AI
Jun 8
Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
arXiv:2606. 07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge.
arXiv AI
Jun 3
Re-Evaluating Continual Learning with Few-Shot Adaptation
arXiv:2606. 03843v1 Announce Type: cross Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks.
arXiv Machine Learning
Aug 18
Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning
arXiv:2608. 16345v1 Announce Type: new Abstract: Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation to new tasks.
arXiv Machine Learning
Aug 12
GLAM: Efficient Continual Learning at Scale via Grouped LoRA Adapter Merging
arXiv:2509. 13211v4 Announce Type: replace Abstract: The ability to learn continuously over time remains a major challenge for modern machine learning systems, even in the era of Foundation Models.
arXiv Machine Learning
Jul 30
The Art of Not Forgetting A Local Learning Architecture for Continual Learning
arXiv:2607. 26523v1 Announce Type: new Abstract: We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?
arXiv Machine Learning
Oct 23
HERD: Harness-Evolving Recursive Distillation for Continual Learning
HERD (Harness-Evolving Recursive Distillation) is a hybrid continual learning framework that combines harness-based non‑parametric methods with parametric distillation. It refines reusable skills in a harness while storing high‑residual examples in bounded memory, and applies drift‑constrained distillation with dual anchors to reduce forgetting. On the 25‑stage TRACE+17 task stream, HERD matches the best parametric baseline in new‑task learning while achieving the lowest forgetting and strongest retention of general capabilities under fixed memory and compute budgets.
arXiv AI
Aug 7
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).
arXiv AI
Jul 22
Parameter-Efficient Continual Fine-Tuning: A Survey
arXiv:2504. 13822v3 Announce Type: replace-cross Abstract: The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance.
arXiv Machine Learning
Jul 28
Gradient-Free Continual Learning
arXiv:2504. 01219v2 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting.
arXiv AI
Jul 22
Soft-TransFormers for Continual Learning
arXiv:2411. 16073v4 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework that adapts a frozen pre-trained Transformer through task-specific soft subnetworks: real-valued multiplicative masks over the query, key, value, and output projections of selected self-attention layers.