Author

Roshan Reddy Upendra

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#artificial intelligence Preprint Oct 2026

RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions

Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. However, adapting existing models to unseen datasets requires tens of thousands of examples. Here we introduce RT-J, an 85M-parameter relational foundation model (RFM) that...

Rishabh Ranjan, Vignesh Kothapalli, Harsh Agarwal et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Model Dependence and Evaluation Reliability

The support/query information boundary is identified as an important component of reliable relational ICL evaluation as well as a concern with native-schema leakage candidates from the original relational schema.

Roshan Reddy Upendra, Alexandre Dorais, Joe Meyer et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Impact, Detection, and Mitigation

This work evaluates a frozen relational encoder with an ICL head on held-out RelBench databases and uses Integrated Gradients to rank and remove suspicious columns, showing that the effect of support-set leakage varies across tasks and relational distances.

Roshan Reddy Upendra, Alexandre Dorais, Joe Meyer et al. · 0 citations