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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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

Aug 13

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 Statistics ML

3d ago

A Dynamical Theory of LoRA in Continual Learning

arXiv:2609.39367v1 Announce Type: new Abstract: Despite the widespread use of Low-Rank Adaptation (LoRA), little is known about its dynamics in continual learning and the mechanisms by which low-rank...

By Th\'eo Marchetta, Filippo Alessandroni, Alessandro Breccia, Alessandro Ingrosso, Federica Gerace

fine-tuning benchmarks

arXiv AI

3d ago

Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics

arXiv:2609.38768v1 Announce Type: cross Abstract: Prior work has shown that neural networks exhibit implicit biases toward low-complexity structure (e.g., spectral bias), memorization dynamics, and c...

By Fujie Gao, Zuyue Zhang, Gang Sun

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

One email a morning, machine-written

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

A Dynamical Theory of LoRA in Continual Learning

Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics

Interference and Retention in Continual Learning

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