arXiv Machine Learning August 18, 2026 By Isabelle Aguilar, Zayn Andre Zainal, Omid Kavehei

Metaplasticity as adaptive gradient preconditioning for incremental learning

Read the original on arXiv Machine Learning →

arXiv:2608. 14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses.

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

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arXiv Machine Learning

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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.

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Theoretical Foundations of Continual Learning via Drift-Plus-Penalty

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NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning

arXiv:2608. 04358v1 Announce Type: new Abstract: Continual learning (CL) requires models to learn tasks sequentially, yet deep neural networks often suffer from plasticity loss and poor knowledge transfer, which can impede their long-term adaptability.

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To Retain or to Adapt? Generalizing Continual Learning

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arXiv AI

Jul 17

NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning

arXiv:2604. 27031v2 Announce Type: replace-cross Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities.

By Karthik Charan Raghunathan, Christian Metzner, Laura Kriener, Melika Payvand

arXiv AI

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Mistake gating leads to energy and memory efficient continual learning

arXiv:2604. 14336v2 Announce Type: replace Abstract: Synaptic plasticity is metabolically expensive, yet animals continuously update their internal models without exhausting energy reserves.

By Aaron Pache, Mark CW van Rossum

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

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