arXiv Machine Learning June 19, 2026 By Jan Wasilewski, J\k{e}drzej Kozal, Micha{\l} Wo\'zniak, Bartosz Krawczyk

Sparsity, Superposition, and Forgetting: A Mechanistic Study of Representation Retention in Continual Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 20431v1 Announce Type: new Abstract: Continual learning (CL) systems often forget previously acquired knowledge, yet the mechanisms driving forgetting remain hard to isolate in practice because real datasets entangle many factors.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

Jun 5

Catastrophic Forgetting as Accessibility Collapse: A Three-Level Framework for Knowledge Persistence in Continual Learning

arXiv:2606. 06032v1 Announce Type: new Abstract: Catastrophic forgetting is commonly interpreted as the irreversible erasure of previously acquired knowledge during sequential learning.

By Ayushman Trivedi, Bhavika Melwani

arXiv Machine Learning

6d ago

Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning

arXiv:2608. 11690v1 Announce Type: new Abstract: Continual learning must absorb new tasks without erasing old ones, and replay---mixing a small buffer of past examples into current training---is among the most effective remedies for catastrophic forgetting.

By Tieliang Gong, Zhongbo Zhang, Wen Wen, Yong-Jin Liu

benchmarks safety

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.

By Julius St\"ork

efficiency benchmarks

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?

By Ashmith Atmuri, Yashaswini Rao Bhogarajula

llms benchmarks

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.

By Haihua Luo, Xuming Ran, Tommi K\"arkk\"ainen, Huiyan Xue, Zhonghua Chen, Qi Xu, Fengyu Cong

fine-tuning benchmarks

arXiv AI

Jun 2

AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents

arXiv:2606. 02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.

By Yiheng Shu, Bernal Jim\'enez Guti\'errez, Saisri Padmaja Jonnalagedda, Yuguang Yao, Huan Sun, Yu Su

agents benchmarks

Related stories

Catastrophic Forgetting as Accessibility Collapse: A Three-Level Framework for Knowledge Persistence in Continual Learning

Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning

Interference and Retention in Continual Learning

The Art of Not Forgetting A Local Learning Architecture for Continual Learning

Representation Finetuning for Continual Learning

AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents