We’re happy to announce our first release in relational learning at Prior Labs, continuing our 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. First and foremost, we release 𝗥𝗲𝗹𝗔𝗿𝗲𝗻𝗮-α: a unified framework for running and comparing baselines on RelBench v1 tasks. Based on learnings from tabular benchmarks like TabArena, we are standardizing data loading, evaluation protocols, tuning regimes, and adding support for systems with custom tuning. We also open-source 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹: our relational harness for TabPFN-3. We initialize the (living) RelArena-α leaderboard with TabPFN-Rel and a comprehensive set of baselines. The rankings at the time of release are: • 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹 is the No. 1 model submission • 𝗥𝗧-𝗣𝗹𝘂𝗥𝗲𝗹 is the No. 1 system submission Last but not least, we open-source an alpha version of the 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 (𝗥𝗣𝗜): enabling you to easily specify prediction tasks on your own relational database and then run any RelArena-α model, like TabPFN-Rel, in a few lines of code, all bundled as a simple PyPI package. • Read the full model report: arxiv.org/abs/2608.16319 • GitHub repository (give us a ⭐️): github.com/PriorLabs/relaren… • Announcement: priorlabs.ai/blog-posts/intr… • Docs: docs.priorlabs.ai/capabiliti… Thanks to the contributions from: @adrihayler, @KNfloege, @AlanArazi1536, @_rishabhranjan_, @jure, @LennartPurucker, @FrankRHutter & @noahholl

We’re happy to announce our first release in relational learning at Prior Labs, continuing our 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. First and foremost, we release 𝗥𝗲𝗹𝗔𝗿𝗲𝗻𝗮-α: a unified framework for running and comparing baselines on RelBench v1 tasks. Based on learnings from tabular benchmarks like TabArena, we are standardizing data loading, evaluation protocols, tuning regimes, and adding support for systems with custom tuning. We also open-source 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹: our relational harness for TabPFN-3. We initialize the (living) RelArena-α leaderboard with TabPFN-Rel and a comprehensive set of baselines. The rankings at the time of release are: • 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹 is the No. 1 model submission • 𝗥𝗧-𝗣𝗹𝘂𝗥𝗲𝗹 is the No. 1 system submission Last but not least, we open-source an alpha version of the 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 (𝗥𝗣𝗜): enabling you to easily specify prediction tasks on your own relational database and then run any RelArena-α model, like TabPFN-Rel, in a few lines of code, all bundled as a simple PyPI package. • Read the full model report: arxiv.org/abs/2608.16319 • GitHub repository (give us a ⭐️): github.com/PriorLabs/relaren… • Announcement: priorlabs.ai/blog-posts/intr… • Docs: docs.priorlabs.ai/capabiliti… Thanks to the contributions from: @adrihayler, @KNfloege, @AlanArazi1536, @_rishabhranjan_, @jure, @LennartPurucker, @FrankRHutter & @noahholl

We’re happy to announce our first release in relational learning at Prior Labs, continuing our 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. First and foremost, we release 𝗥𝗲𝗹𝗔𝗿𝗲𝗻𝗮-α: a unified framework for running and comparing baselines on RelBench v1 tasks. Based on learnings from tabular benchmarks like TabArena, we are standardizing data loading, evaluation protocols, tuning regimes, and adding support for systems with custom tuning. We also open-source 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹: our relational harness for TabPFN-3. We initialize the (living) RelArena-α leaderboard with TabPFN-Rel and a comprehensive set of baselines. The rankings at the time of release are: • 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹 is the No. 1 model submission • 𝗥𝗧-𝗣𝗹𝘂𝗥𝗲𝗹 is the No. 1 system submission Last but not least, we open-source an alpha version of the 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 (𝗥𝗣𝗜): enabling you to easily specify prediction tasks on your own relational database and then run any RelArena-α model, like TabPFN-Rel, in a few lines of code, all bundled as a simple PyPI package. • Read the full model report: arxiv.org/abs/2608.16319 • GitHub repository (give us a ⭐️): github.com/PriorLabs/relaren… • Announcement: priorlabs.ai/blog-posts/intr… • Docs: docs.priorlabs.ai/capabiliti… Thanks to the contributions from: @adrihayler, @KNfloege, @AlanArazi1536, @_rishabhranjan_, @jure, @LennartPurucker, @FrankRHutter & @noahholl

🎉🎉 PluRel will be presented at #ICML2026 🎉🎉 Date: Tue, Jul 7, 2026 • 10:30 AM – 12:15 PM KST Location: HALL A #2715 by co-author: @_rishabhranjan_ ! Also checkout our latest website for interactive visualizations, access to code, models and data: star-project.stanford.edu/pl…

Introducing ABC: open data, training, and infrastructure for robotics. We release the largest teleop dataset to date, and extensively investigate design decisions, pretraining, and post-training techniques. @arthurallshire @Cinnabar233 @adamrasb @redstone_hong @davidrmcall

Relational Foundation Models face a scaling problem: diverse training datasets are rarely public due to privacy constraints 🔒. 🚀 We are excited to introduce "PluRel": a framework that synthesizes diverse multi-table relational databases from scratch, unlocking scaling laws for RFMs. 🧵 Kudos to the amazing collaborators at @StanfordAILab @Kumo_ai_team , and @SAP : @_rishabhranjan_ @VHudovernik @vijaypradwi @johanneshoffart @guestrin @jure

Come check out PluRel at the DATA-FM workshop @iclr_conf tomorrow (04/26) Room 203 A/B

Although relational databases are everywhere, there is no equivalent of the public internet for pretraining Relational Foundation Models (RFMs). Excited to see RelBench bridging that gap, growing from 7 datasets in v1 to 88+ datasets in v2. Deeply grateful to the numerous community contributions for helping RelBench serve as the central data repository for RFM research. ❤️

Transformers are great for sequences, but most business-critical predictions (e.g. product sales, customer churn, ad CTR, in-hospital mortality) rely on highly-structured relational data where signal is scattered across rows, columns, linked tables and time. Excited to finally share what I have been working on over the last year: a Foundation Model architecture which brings the power of Transformers to relational domains, enabling large-scale pretraining and zero-shot generalization in enterprise settings. 🧵1/n

📚 Today at the Reading Group, Thu, Feb 26, 11am EST, we’re excited to host Vignesh Kothapalli @kvignesh1420 (Stanford University) presenting: PLUREL: Synthetic Data Unlocks Scaling Laws for Relational Foundation Models zoom link on our website See you there! 🚀

This Thursday (Feb 19, 11am EST) at the reading group: Rishabh Ranjan (Stanford) presents Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data. Paper & code: github.com/snap-stanford/rel… Hope to see you there! zoom link on website!

This Thursday (Feb 19, 11am EST) at the reading group: Rishabh Ranjan (Stanford) presents Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data. Paper & code: github.com/snap-stanford/rel… Hope to see you there! zoom link on website!

Relational Foundation Models face a scaling problem: diverse training datasets are rarely public due to privacy constraints 🔒. 🚀 We are excited to introduce "PluRel": a framework that synthesizes diverse multi-table relational databases from scratch, unlocking scaling laws for RFMs. 🧵 Kudos to the amazing collaborators at @StanfordAILab @Kumo_ai_team , and @SAP : @_rishabhranjan_ @VHudovernik @vijaypradwi @johanneshoffart @guestrin @jure