arXiv Machine Learning October 3, 2026 By Simon Forbat, Rainer Gemulla

RelICL: Training-free Relational Learning with Tabular Foundation Models

Read the original on arXiv Machine Learning →

RelICL: Training-free Relational Learning with Tabular Foundation Models proposes a new method for relational learning that addresses two key issues of deep feature synthesis—feature explosion and interaction blindness—by propagating and fusing information step by step through the schema graph using a tabular foundation model. The approach retains the benefits of DFS while improving scalability and performance. Experiments on RelBench tasks show that RelICL performs on par with the strongest DFS-based approach.

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