arXiv Machine Learning July 23, 2026 By Md Hasibul Amin, Tamzid Tanvi Alam

Failure Modes of Always-On Inter-Cluster Repulsion in Replay-Based Continual Learning

Read the original on arXiv Machine Learning →

arXiv:2510. 07648v3 Announce Type: replace Abstract: Feature-space objectives are often added to replay-based continual learning systems with the expectation that better geometric separation will improve retention.

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.

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 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

4d ago

ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning

arXiv:2609.38833v1 Announce Type: new Abstract: Federated continual learning must integrate new tasks over time without losing earlier-task knowledge. Most existing methods attach an anti-forgetting...

By Sungmin Kang, Zhengzhong Tu, Sunwoo Lee

arXiv Machine Learning

Jun 2

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning

arXiv:2606. 01379v1 Announce Type: new Abstract: While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limits later tasks from improving earlier ones, leaving backward knowledge transfer underexplored.

By Anushka Tiwari, Kaiyi Ji

llms benchmarks

arXiv Machine Learning

6d ago

Neural Succession: A Mesoscopic Theory of Invasion, Coexistence, and Stabilization in Continual Learning

arXiv:2609.36375v1 Announce Type: new Abstract: Continual learning is usually studied through mechanisms that preserve old knowledge. We develop Successional Learning Theory (SLT), a mesoscopic accou...

By Shoaib Ahmed Dipu, Md Salman Shamil, Sayeed Shafayet Chowdhury

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.

By Byeong Hoon Yoon

rag fine-tuning efficiency benchmarks

One email a morning, machine-written

Related stories

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

Interference and Retention in Continual Learning

ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning

Neural Succession: A Mesoscopic Theory of Invasion, Coexistence, and Stabilization in Continual Learning

Neural Subspace Reallocation: Continual Learning as Retrieval-Based Subspace Memory Management