Hugging Face Trending Papers

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

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.

arXiv Machine Learning

1d ago

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

benchmarks

arXiv Machine Learning

Aug 3

PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining

arXiv:2607. 29129v1 Announce Type: new Abstract: Relational Foundation Models (RFMs) require large-scale synthetic relational databases for pretraining, but existing approaches tightly couple data generation with the model training pipeline.

By Mohammad Sadeq Abolhasani, Viswanath Ganapathy

benchmarks

arXiv AI

Jul 7

A Fair Benchmarking of Deep Relational Database Learning Models

arXiv:2607. 03659v1 Announce Type: cross Abstract: Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult.

By Kazi F. Akhter, Bharath Ajendla, Manar D. Samad

llms benchmarks

arXiv AI

Jun 9

AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

arXiv:2510. 18428v4 Announce Type: replace Abstract: Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise mathematical formulations and executable solver code.

By Minwei Kong, Ao Qu, Xiaotong Guo, Wenbin Ouyang, Chonghe Jiang, Han Zheng, Yining Ma, Dingyi Zhuang, Yuhan Tang, Junyi Li, Shenhao Wang, Haris Koutsopoulos, Hai Wang, Cathy Wu, Jinhua Zhao

llms benchmarks

arXiv AI

Jul 15

PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

arXiv:2602. 04029v2 Announce Type: replace-cross Abstract: Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases.

By Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik, Vijay Prakash Dwivedi, Johannes Hoffart, Carlos Guestrin, Jure Leskovec

safety

arXiv AI

Jun 3

Evaluating Relational Reasoning in LLMs with REL

arXiv:2604. 12176v2 Announce Type: replace Abstract: Relational reasoning is the ability to infer relations that jointly bind multiple entities, attributes, or variables.

By Lukas Fesser, Yasha Ektefaie, Ada Fang, Sham M. Kakade, Marinka Zitnik

llms benchmarks

arXiv Machine Learning

Aug 3

Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data

arXiv:2607. 29120v1 Announce Type: new Abstract: Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on millions of synthetic tasks.

By Mohammad Sadeq Abolhasani, Viswanath Ganapathy

benchmarks

arXiv AI

Jul 28

Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering

arXiv:2607. 22633v1 Announce Type: new Abstract: Table Question Answering (TableQA) aims to reason over tables to answer user queries.

By Guixin Su, Qiankun Pi, Mayi Xu, Wenli Li, Ming Zhong, Yuanyuan Zhu, Jiawei Jiang, Tieyun Qian

llms nlp efficiency

arXiv AI

Jun 30

Database Context Compression for Text-to-SQL on Real-World Large Databases

arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.

By Jingwen Liu, Weibin Liao, Xin Gao, Junfeng Zhao, Yasha Wang

llms benchmarks data-engineering

arXiv AI

Jun 9

Generative Reasoning Re-ranker

arXiv:2602. 07774v5 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge.

By Mingfu Liang, Yufei Li, Jay Xu, Kavosh Asadi, Xi Liu, Shuo Gu, Kaushik Rangadurai, Frank Shyu, Shuaiwen Wang, Song Yang, Zhijing Li, Jiang Liu, Mengying Sun, Fei Tian, Xiaohan Wei, Chonglin Sun, Jacob Tao, Shike Mei, Wenlin Chen, Santanu Kolay, Sandeep Pandey, Hamed Firooz, Luke Simon

llms rag nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 7

TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data

arXiv:2607. 03926v1 Announce Type: cross Abstract: Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows.

By Jialin Zhang, Fenghao Dong, Yajie Zhou, Vyas Sekar, Shinan Liu

benchmarks safety data-engineering

arXiv Machine Learning

Jul 21

Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data

arXiv:2602. 02025v2 Announce Type: replace-cross Abstract: ML models critically depend on feature quality, yet in real-world settings, useful features are often distributed across multiple relational tables rather than a single dataset.

By Serafeim Papadias, Kostas Patroumpas, Dimitrios Skoutas

llms benchmarks

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