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arXiv · 2610.06751

MatrixFormer: A Foundation Model for Matrix Completion

Dwaipayan Saha·Jacob Feitelberg·Kyuseong Choi·Raaz Dwivedi·Anish Agarwal
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Abstract

Matrix completion underlies problems from tabular imputation to causal inference, yet existing tabular foundation models treat it as entry-by-entry prediction, repeating context for every target and discarding the matrix's two-dimensional structure. We introduce MatrixFormer, a pre-trained matrix-native transformer that predicts a full distribution for every missing entry in a single forward pass. MatrixFormer is trained entirely on synthetic low-rank and latent-factor matrices under diverse missingness patterns. Applied zero-shot and with the same model weights, MatrixFormer achieves competitive performance on causal inference panel-data tasks, language-model benchmark-score completion, tabular imputation, and recommendation systems matrix completion. These results position MatrixFormer as a general-purpose foundation model for matrix completion.

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Dwaipayan Saha, Jacob Feitelberg, Kyuseong Choi, Raaz Dwivedi, Anish Agarwal. 2026-10-05. MatrixFormer: A Foundation Model for Matrix Completion. https://arxiv.org/abs/2610.06751

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