titleAdvancing Open and Reproducible Relational Learning: RelArena-α, TabPFN-Rel and RPI
authorsAdrian Hayler, Klemens Flöge, Alan Arazi, Rishabh Ranjan, Jure Leskovec, Felix Birkel, Brendan Roof, Anurag Garg, Kristina Collins, Lydia Sidhoum, Jonas Kübler, Siyuan Guo, Oscar Key, Jan Hendrik Metzen, Rylee Grace, David Salinas, Arthur Cahu, Simon Bing, Benjamin Jäger, Tuana Çelik, Mihir Manium, Vitor Monteiro, Jake Robertson, Jerry Chen, Eliott Kalfon, Tomás Pereda, Lilly Wehrhahn, Dominik Safaric, Tobias Schroeder, Georg Grab, Diana Kriuchkova, Clara Cornu, Philipp Singer, Nick Erickson, Vahid Balazadeh, Marie Salmon, Simone Alessi, Kürşat Kaya, Philipp Jund, Léo Grinsztajn, Yann LeCun, Bernhard Schölkopf, Madelon Hulsebos, Lennart Purucker, Sauraj Gambhir, Frank Hutter, Noah Hollmann
date2026-08-17
venuearXiv (preprint, not peer reviewed)
idarXiv:2608.16319
affiliationPrior Labs
upvotes31 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractThis first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate research in the field towards meaningful real-world impact. We aim to steer further development based on feedback from, and in collaboration with, the community. Given the early stage of development, our α-release targets researchers and early-adopting practitioners. Over the past years, a variety of datasets and tasks for relational learning have emerged, but the community has not converged on a reliable, reproducible way to compare different methods on these tasks. Our α-release, RelArena-α, provides a unified framework for running and comparing baselines on RelBench v1 by standardizing data loading, evaluation protocols, tuning regimes, and support for systems with custom tuning, inspired by established tabular benchmarks such as TabArena. We plan to work with the research community to further develop RelArena-α into a catalyst for progress in the relational learning community. We release the initial version of TabPFN-Rel, a purpose-built relational harness for TabPFN-3. Currently ranked first among models on RelArena-α, TabPFN-Rel makes key improvements upon RDBLearn. Beyond its ranking, TabPFN-Rel serves as a strong baseline, adding to the growing evidence that flattening a relational database into a single table remains competitive with specialized relational architectures on real-world tasks. To facilitate adoption of relational learning methods in research and industry, we release an initial α-version of our Relational Predictive Interface, RPI, an open-source, model-agnostic interface that enables early adopters to easily define problems on new databases and apply any model implemented in RelArena-α, including TabPFN-Rel, to these problems.

cite

@misc{hayler2026advancing,
  title={Advancing Open and Reproducible Relational Learning: RelArena-α, TabPFN-Rel and RPI},
  author={Adrian Hayler and Klemens Flöge and Alan Arazi and Rishabh Ranjan and Jure Leskovec and Felix Birkel and Brendan Roof and Anurag Garg and Kristina Collins and Lydia Sidhoum and Jonas Kübler and Siyuan Guo and Oscar Key and Jan Hendrik Metzen and Rylee Grace and David Salinas and Arthur Cahu and Simon Bing and Benjamin Jäger and Tuana Çelik and Mihir Manium and Vitor Monteiro and Jake Robertson and Jerry Chen and Eliott Kalfon and Tomás Pereda and Lilly Wehrhahn and Dominik Safaric and Tobias Schroeder and Georg Grab and Diana Kriuchkova and Clara Cornu and Philipp Singer and Nick Erickson and Vahid Balazadeh and Marie Salmon and Simone Alessi and Kürşat Kaya and Philipp Jund and Léo Grinsztajn and Yann LeCun and Bernhard Schölkopf and Madelon Hulsebos and Lennart Purucker and Sauraj Gambhir and Frank Hutter and Noah Hollmann},
  year={2026},
  eprint={2608.16319},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2608.16319}
}