推进开放和可复现的关系学习：RelArena-α、TabPFN-Rel与RPI
Advancing Open and Reproducible Relational Learning: RelArena-α, TabPFN-Rel and RPI
August 17, 2026
作者: Adrian 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
cs.AI
摘要
Prior Labs 在关系学习领域的首次发布展现了我们对开放科学的持续承诺。我们开源三款软件，期望能加速该领域研究，推动其产生切实的现实世界影响。我们旨在根据社区的反馈并与社区合作，引导后续开发方向。鉴于当前仍处于早期开发阶段，我们的 α 版本主要面向研究人员和早期采用者。过去几年中，关系学习领域涌现了多种数据集和任务，但社区尚未就这些任务上不同方法的可靠、可复现比较方式达成共识。我们的 α 版本 RelArena-α 提供了一个统一框架，通过在 RelBench v1 上标准化数据加载、评估协议、调优机制，并支持自定义调优的体系，来运行和比较各基线方法，其灵感来源于 TabArena 等成熟的表格基准。我们计划与研究社区合作，进一步将 RelArena-α 发展为推动关系学习社区进步的催化剂。我们同时发布了 TabPFN-Rel 的初始版本，这是一个专为 TabPFN-3 设计的关系学习工具框架。TabPFN-Rel 目前在 RelArena-α 上排名第一，并在 RDBLearn 基础上做出了关键改进。除了排名优势之外，TabPFN-Rel 还作为一个强基线，进一步印证了将关系数据库展平为单一表格的方法在现实任务中仍能与专门的关系架构相竞争。
为了促进关系学习方法在研究和工业界的应用，我们发布了关系预测接口（Relational Predictive Interface, RPI）的初始 α 版本。RPI 是一个开源、与模型无关的接口，使早期采用者能够轻松地在新的数据库上定义问题，并应用 RelArena-α 中实现的任何模型（包括 TabPFN-Rel）来解决这些问题。
English
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. 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.