arXiv Machine Learning
Early Learning Shapes Later Directions Of Representation Change In Continual Learning
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Multi-Scale Structural Features for Continual, Comprehensible Visual Recognition in a Developmental Learning Framework
arXiv:2607. 25531v1 Announce Type: cross Abstract: Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure.
arXiv AI
Jun 17
Dimensionality Controls When Modularity Helps in Continual Learning
arXiv:2606. 17889v1 Announce Type: cross Abstract: Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tasks share structure and risk interference.
arXiv Machine Learning
Jun 19
Sparsity, Superposition, and Forgetting: A Mechanistic Study of Representation Retention in Continual 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.
arXiv Machine Learning
Aug 18
Geometry of Forgetting: Representation Flux in Continual Learning
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.
arXiv Computer Vision
Aug 27
A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training
The paper introduces a new task called Multimodal Unsupervised Continual Post-Training (MU‑CPT), which allows multimodal large language models (MLLMs) to continuously learn from streaming unlabeled data. It identifies token‑level visual dependence (VD) as essential for MU‑CPT, using its structural distortion to detect cross‑modal forgetting and its heterogeneity to guide new‑task learning. The proposed Visual Dependence‑Aware (VDA) framework includes Visually Constrained Optimal Transport (VC‑OT) to mitigate forgetting and Visually Modulated Adaptation (VMA) to enhance new‑task plasticity, achieving a balance between stability and adaptability in MU‑CPT.
arXiv Machine Learning
1d ago
Behavioral Convergence Without Representational Convergence: Persistent Training-History Dependence in Neural Networks
arXiv:2609.37836v1 Announce Type: new Abstract: Neural networks trained toward the same final objective can reach similar predictive performance while retaining internal representations shaped by ear...