TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
The paper introduces TabPFN-MT, a multitask in-context learner designed for tabular data. This model improves upon prior-data fitted networks by enabling simultaneous inference across multiple tasks while maintaining computational efficiency. Extensive evaluations show that TabPFN-MT achieves state-of-the-art performance in deep tabular multitask learning.
- ▪TabPFN-MT is trained on an expanded multi-target synthetic prior to capture inter-task dependencies.
- ▪The model reduces the inference cost for multiple tasks from O(T) to O(1) forward passes.
- ▪It establishes a new state-of-the-art for deep tabular multitask learning across 344 datasets.
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Computer Science > Machine Learning arXiv:2605.20234 (cs) [Submitted on 16 May 2026] Title:TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data Authors:Cormac Cureton, Narges Armanfard View a PDF of the paper titled TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data, by Cormac Cureton and Narges Armanfard View PDF HTML (experimental) Abstract:Prior-Data Fitted networks (PFNs) have been very successful in tabular contexts, handling prediction tasks in context. However, they are designed for single-task inference, meaning that predicting several target values within a context requires repeated forward calls and precludes inter-task information sharing.
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