arXiv Machine Learning June 2, 2026 By Matthew Farrugia-Roberts

Structure and Scale in Simplicial Sequence Modelling

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arXiv:2606. 01302v1 Announce Type: new Abstract: Modern large-scale deep learning exhibits two striking empirical phenomena: behavioural scaling laws (predictable performance gains with increasing scale) and emergent mechanisms (structured internal representations and circuits in deep neural networks).

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From Approximation to Emergence: A Theory of Deep Learning

arXiv:2607. 01311v1 Announce Type: new Abstract: Deep learning has outgrown any single mathematical explanation.

By Zhilin Zhao

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Adaptive inference and function vectors in deep transformers

arXiv:2606. 16694v1 Announce Type: cross Abstract: Transformers are widely used as a general-purpose substrate for learning complex correlations between a large collection of coupled variables, but their internal mechanisms have remained mysterious.

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A Mechanistic Study of Transformers Training Dynamics

arXiv:2410. 24050v3 Announce Type: replace Abstract: Large-scale pretraining of transformers has been central to the success of foundation models.

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Fractal basin geometry and the limits to predictability and reproducibility in deep learning

arXiv:2510.05606v2 Announce Type: replace Abstract: Fundamental limits to predictability are central to our understanding of many physical and computational systems. In deep learning, training outcom...

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DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining

arXiv:2606. 14283v1 Announce Type: cross Abstract: Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring.

By Johannes De Smedt, Jari Peeperkorn, Artem Polyvyanyy, Jochen De Weerdt

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