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.
By Jan Wasilewski, J\k{e}drzej Kozal, Micha{\l} Wo\'zniak, Bartosz Krawczyk
arXiv:2609.36081v1 Announce Type: new
Abstract: Representations continually change as a network learns new tasks. We ask whether early representational changes naturally form a geometric structure th...
By Yuantao Deng, Jinnuo Liu, Kaizhen Tan, Yuchen Liu
arXiv:2607. 25531v1 Announce Type: cross Abstract: Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure.
By Zeki Doruk Erden
arXiv:2609.25146v1 Announce Type: new
Abstract: Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems ope...
By Hongwei Yan, Kanglei Zhou, Qi Cheng, Weiyi Dong, Chunyan Lan, Guanglong Sun, Jun Zhou, Qian Li, Yi Zhong, Liyuan Wang
The paper introduces a compositional continual learning benchmark for world models in robot manipulation, designed to isolate knowledge reuse from learning speed and capacity. Tasks are curated to combine previously seen action and perception components, allowing analysis of how different modalities affect reuse. Experiments show that modular world models better balance reuse and forgetting than conventional methods, yet none fully solve the challenge, highlighting the need for models explicitly built to reuse knowledge without forgetting.
By Haoyu Zhou, Joe Watson, Anson Lei, Ingmar Posner
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.
By Maksim A. Kazanskii