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Advancing Open and Reproducible Relational Learning: RelArena-$α$, TabPFN-Rel and RPI

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This 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.

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arXiv Machine Learning
Aug 18

Advancing Open and Reproducible Relational Learning: RelArena-$\alpha$, TabPFN-Rel and RPI

arXiv:2608. 16319v1 Announce Type: new Abstract: This first release of Prior Labs in relational learning shows our continued commitment to open science.

By Adrian Hayler, Klemens Fl\"oge, Alan Arazi, Rishabh Ranjan, Jure Leskovec, Felix Birkel, Brendan Roof, Anurag Garg, Kristina Collins, Lydia Sidhoum, Jonas K\"ubler, Siyuan Guo, Oscar Key, Jan Hendrik Metzen, Rylee Grace, David Salinas, Arthur Cahu, Simon Bing, Benjamin J\"ager, Tuana \c{C}elik, Mihir Manium, Vitor Monteiro, Jake Robertson, Jerry Chen, Eliott Kalfon, Tom\'as Pereda, Lilly Wehrhahn, Dominik Safaric, Tobias Schroeder, Georg Grab, Diana Kriuchkova, Clara Cornu, Philipp Singer, Nick Erickson, Vahid Balazadeh, Marie Salmon, Simone Alessi, K\"ur\c{s}at Kaya, Philipp Jund, L\'eo Grinsztajn, Yann LeCun, Bernhard Sch\"olkopf, Madelon Hulsebos, Lennart Purucker, Sauraj Gambhir, Frank Hutter, Noah Hollmann
arXiv AI
Sep 10

Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?

The paper introduces Open Tabular Insight Extraction (OpenTI), a unified framework aimed at democratizing access to insights from large table corpora. It highlights how current research is fragmented across domains like table QA, text‑to‑SQL, and data analysis agents, and shows that existing systems and benchmarks fall short of covering the full end‑to‑end scope of OpenTI. The authors propose a consolidated terminology, conduct a systematic review, and outline a research agenda for developing comprehensive OpenTI systems, evaluation methods, and interaction paradigms.

By Daniel Gomm, Maarten de Rijke, Madelon Hulsebos