TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
TabPFN-MT is a multitask in-context learning model for tabular data that extends Prior-Data Fitted networks (PFNs) with an expanded multi-target synthetic prior and shared decoder, enabling simultaneous inference across tasks and reducing inference cost from O(T) to O(1). It achieves state-of-the-art results on small-to-medium datasets.
[2605.20234] TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
[Submitted on 16 May 2026]
Title:TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
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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. We propose TabPFN-MT, which is trained on an expanded multi-target synthetic prior to capture inter-task dependencies in context. This model uses an expanded $y$-encoder and a shared decoder head to enable multitask in-context learning and simultaneous inference. The model is uniquely specialized for small-to-medium datasets by relying on in-context learning rather than traditional gradient-based training. Within this regime (averaging fewer than 1,000 samples), extensive evaluations across 344 datasets demonstrate that TabPFN-MT establishes a new state-of-the-art for deep tabular multitask learning. Furthermore, despite the inherent compute asymmetry of joint optimization, our model remains highly competitive with the latest state-of-the-art single-task ensembles. Notably, on multitask datasets it achieves an overall Accuracy rank of 4.89, the highest average rank among all models tested. Crucially, TabPFN-MT delivers this highly competitive performance while reducing the inference cost for $T$ tasks from $O(T)$ to $O(1)$ forward passes, offering a massive computational efficiency improvement for multi-target tabular applications.
Comments: 24 pages, 7 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.20234 [cs.LG]
(or arXiv:2605.20234v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.20234
arXiv-issued DOI via DataCite
Submission history
From: Cormac Cureton [view email] [v1] Sat, 16 May 2026 15:02:03 UTC (1,683 KB)
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