AI RESEARCH
TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
arXiv CS.LG
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ArXi:2605.20234v1 Announce Type: new 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.