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

Structure and Scale in Simplicial Sequence Modelling

Read the original on arXiv Machine Learning →

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).

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

Jul 3

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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arXiv AI

Jun 16

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.

By Ravin Raj, Gautam Reddy

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arXiv Machine Learning

Jun 30

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.

By Ambroise Odonnat, Wassim Bouaziz, Vivien Cabannes

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arXiv Machine Learning

Jun 19

A Unified Perspective on the Dynamics of Deep Transformers

arXiv:2501. 18322v2 Announce Type: replace Abstract: Transformers, which are state-of-the-art in most machine learning tasks, represent the data as sequences of vectors called tokens.

By Val\'erie Castin, Pierre Ablin, Jos\'e Antonio Carrillo, Gabriel Peyr\'e

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arXiv AI

Jun 15

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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arXiv Machine Learning

Jun 25

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns

arXiv:2606. 25010v1 Announce Type: new Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabilities such as in-context learning are known to emerge abruptly past a certain model scale.

By Vatsal Baherwani, Zixi Chen, Shikai Qiu, Andrew Gordon Wilson, Pavel Izmailov

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