Is In-Context Universality Enough? MLPs are Also Universal In-Context

Anastasis Kratsios; Takashi Furuya

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arXiv · 2502.03327

Is In-Context Universality Enough? MLPs are Also Universal In-Context

Anastasis Kratsios·Takashi Furuya

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Abstract

The success of transformers is often linked to their ability to perform in-context learning. Recent work shows that transformers are universal in context, capable of approximating any real-valued continuous function of a context (a probability measure over $\mathcal{X}\subseteq \mathbb{R}^d$) and a query $x\in \mathcal{X}$. This raises the question: Does in-context universality explain their advantage over classical models? We answer this in the negative by proving that MLPs with trainable activation functions are also universal in-context. This suggests the transformer's success is likely due to other factors like inductive bias or training stability.

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BibTeX RIS

Anastasis Kratsios, Takashi Furuya. 2025-02-05. Is In-Context Universality Enough? MLPs are Also Universal In-Context. https://arxiv.org/abs/2502.03327

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