arXiv AI
Jul 13
Interference and Retention in Continual Learning
arXiv:2607. 09202v1 Announce Type: cross Abstract: Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation.
Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning
arXiv:2607. 23837v1 Announce Type: new Abstract: Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forgetting.
Related stories
arXiv AI
Jul 20
PASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual Learning
arXiv:2601. 13020v2 Announce Type: replace-cross Abstract: Continual instruction tuning (CIT) requires multimodal large language models (MLLMs) to adapt to a stream of tasks without forgetting prior capabilities.
arXiv AI
6d ago
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.
arXiv Machine Learning
1d ago
Geometry of Forgetting: Representation Flux in Continual Learning
arXiv:2608. 15854v1 Announce Type: new Abstract: Catastrophic forgetting remains a fundamental obstacle to continual learning, where neural networks lose previously acquired knowledge while learning new tasks.
arXiv Machine Learning
Jun 29
When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning
arXiv:2606. 28117v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become the standard tool for parameter-efficient fine-tuning of large pretrained models.
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
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 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
Jun 26
From Weights to Features: SAE-Guided Activation Regularization for LLM Continual Learning
arXiv:2606. 26629v1 Announce Type: new Abstract: Weight-space regularization methods such as Elastic Weight Consolidation (EWC) are the standard approach to catastrophic forgetting in continual learning.
arXiv Machine Learning
Jun 29
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
arXiv:2506. 10355v2 Announce Type: replace Abstract: Many real-world applications collect data in a streaming environment, where learning tasks are encountered sequentially.
arXiv AI
Jul 15
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
arXiv:2607. 12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations.
arXiv AI
Jun 30
Neural Subspace Reallocation: Continual Learning as Retrieval-Based Subspace Memory Management
arXiv:2606. 30067v1 Announce Type: cross Abstract: We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces.