arXiv Machine Learning September 30, 2026 By Ertu\u{g}rul Mutlu

Behavioral Convergence Without Representational Convergence: Persistent Training-History Dependence in Neural Networks

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arXiv Machine Learning

Aug 24

Training, learning and inference: unified dynamics of neural systems

arXiv:2608.20965v1 Announce Type: new Abstract: We define an atomic generation fact f=(u,tau,omega,z;rho), recording the origin, realized transformation, concrete occurrence, generated result and rel...

By Mian Wang

diffusion

arXiv Machine Learning

1d ago

Early Learning Shapes Later Directions Of Representation Change In Continual Learning

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 Machine Learning

Jun 18

Decomposing Prediction Mechanisms for In-Context Recall

arXiv:2507. 01414v2 Announce Type: replace Abstract: We introduce a new family of toy problems that combine features of linear-regression-style continuous in-context learning (ICL) with discrete associative recall.

By Sultan Daniels, Dylan Davis, Dhruv Gautam, Wentinn Liao, Gireeja Ranade, Anant Sahai

llms efficiency

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 Machine Learning

1d 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 Machine Learning

Aug 17

Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules

arXiv:2605. 30556v2 Announce Type: replace Abstract: CORRECTION (August 2026): the central finding of this paper is not supported.

By Nils Leutenegger

safety

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Related stories

Training, learning and inference: unified dynamics of neural systems

Early Learning Shapes Later Directions Of Representation Change In Continual Learning

Decomposing Prediction Mechanisms for In-Context Recall

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

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

Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning Rules