arXiv Machine Learning June 25, 2026 By Vatsal Baherwani, Zixi Chen, Shikai Qiu, Andrew Gordon Wilson, Pavel Izmailov

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns

Read the original on arXiv Machine Learning →

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.

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arXiv Machine Learning

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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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arXiv:2602. 19143v2 Announce Type: replace Abstract: This paper studies simple transformers trained on a high-order Markov chain, where the model must incorporate information from multiple past positions, each with different statistical importance.

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arXiv Machine Learning

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arXiv Machine Learning

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arXiv:2607. 03660v1 Announce Type: cross Abstract: Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt.

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arXiv Machine Learning

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