Relational Deep Learning

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9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

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Revisiting Simple Baselines for Relational Deep Learning
Oct 14, 2026Viet The Nguyen, Vijay Prakash Dwivedi, Gunther GustRelational Deep Learning
University of Würzburg · Seamless Labs, Inc. · Stanford University
RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions
Oct 13, 2026Rishabh Ranjan, Vignesh Kothapalli, Harshvardhan Agarwal +5Relational Foundation Models Relational Deep Learning
Stanford University · SAP
Relational Learning as World Modeling
Oct 12, 2026Zheyu Zhang, Gjergji KasneciAction-Conditioned World Models World Model-Based Planning
Technical University of Munich Munich Center for Machine Learning (MCML)
DuRe-ST: Dual-Relation Spectro-Temporal Modeling for Speech Deepfake Detection
Oct 7, 2026Shaole Li, Siqing Qin, Youzhi Tu +1Audio Deepfake Detection ASVspoof
Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University, Hong Kong SAR
ReGraph: A Computational Account of Emergent Generalization in the "what" and "where" Dual Visual Streams
Oct 6, 2026Hyewon Kang, Jungmin Lee, Ilgyu Lee +1Spatial Generalization Visual Neuroscience
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea · Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Republic of Korea · Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
RelICL: Training-free Relational Learning with Tabular Foundation Models
Oct 1, 2026Simon Forbat, Rainer GemullaTabular Foundation Models Feature Interaction Modeling
Data and Web Science Group University of Mannheim Mannheim, Germany
STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling
Oct 1, 2026Abdalla Mohamed, Ashraf AboulnagaRelational Foundation Models Efficient Inference
University of Texas at Arlington
Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Model Dependence and Evaluation Reliability
Sep 29, 2026Roshan Reddy Upendra, Alexandre Dorais, Joe Meyer +6Relational Foundation Models Data Leakage
SAP Palo Alto, CA, USA · SAP Montreal, Canada · SAP Seattle, WA, USA
Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Impact, Detection, and Mitigation
Sep 28, 2026Roshan Reddy Upendra, Alexandre Dorais, Joe Meyer +5Relational Foundation Models Data Leakage
SAP
Temporal Heterogeneous Graph Pretraining for Relational Deep Learning
Sep 28, 2026Yixin Peng, Er Jin, Diego Collarana +1Temporal GNNs Heterogeneous GNNs
RWTH Aachen University, Aachen, Germany · Fraunhofer FIT, Sankt Augustin, Germany
QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs
Sep 22, 2026Kyaw Hpone Myint, Nan Jiang, Xiang Li +4Temporal GNNs Transformer Attention
Capital One
MetaRTL: Meta-path Attention Enhanced Relational Table Learning
Sep 17, 2026Ken Zhong, Weichen Li, Zheng WangRelational Deep Learning Relational Databases
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China
InRTL: Effective Intra-Inter Interaction Learning for Relational Tables
Sep 11, 2026Weichen Li, Ken Zhong, Zheng Wang +2Tabular Representation Learning Relational Deep Learning
Shanghai Jiao Tong University Shanghai, China
Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning
Sep 1, 2026Oleksii Kolesnichenko, Jakub Peleška, Gustav ŠírRelational Deep Learning
Czech Technical University in Prague, Karlovo náměstí 13, Prague, 121 35, Czechia
Context Window Failures in Relational Foundation Models
Aug 31, 2026Denis Oliveira Correa, Francisco Galuppo AzevedoRelational Foundation Models Graph Foundation Models
Kunumi Institute, Brazil · Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Can LLMs Use Relational Transformer Embeddings?
Aug 31, 2026Francisco Galuppo Azevedo, Clarissa Lima LouresTransformer LLM Fine-Tuning
Kunumi Institute, Brazil · Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection
Aug 26, 2026Fahim Shahriar Khan, Ashraf AboulnagaRelational Deep Learning Graph Neural Networks
University of Texas at Arlington
RAD: Rule-Augmented Relational Anomaly Detection
Aug 24, 2026Noah Dahle, Anne Tumlin, Ngoc Tran +2Anomaly Detection Interpretable Anomaly Detection
Vanderbilt University Nashville, Tennessee, USA
Advancing Open and Reproducible Relational Learning: RelArena- $α$ , TabPFN-Rel and RPI
Aug 17, 2026Adrian Hayler, Klemens Flöge, Alan Arazi +44Relational Deep Learning Relational Databases
Incremental Evaluation and Training in Relational Deep Learning
Aug 13, 2026Jakub Peleška, Gustav ŠírConcept Drift Online Continual Learning
Czech Technical University in Prague
EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction
Aug 13, 2026Danyu Li, Ling Zhou, Rubing Huang +3Graph Structure Learning Heterogeneous GNNs
School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR, 999078, China · Macau University of Science and Technology Zhuhai Research Institute, Zhuhai, Guangdong, 519099, China · Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, Hubei, 430070, China +2
TRCoRSurg: Temporal-Relational Co-Reasoning for Surgical Video Triplet Recognition
Aug 5, 2026Fang Li, Shihao Zou, Weixin Si +3Surgical Video Understanding Relational Deep Learning
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology
Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data
Jul 31, 2026Mohammad Sadeq Abolhasani, Viswanath GanapathySynthetic Data Pretraining Synthetic Data Generation
SAP Labs, LLC. Palo Alto, CA, United States
Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraints
Jul 8, 2026Alan Gany, Bogdan Cautis, Silviu ManiuGraph Neural Networks Adversarial Attacks
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG Grenoble, France
A Fair Benchmarking of Deep Relational Database Learning Models
Jul 4, 2026Kazi F. Akhter, Bharath Ajendla, Manar D. SamadTransformer Graph Representation Learning
Department of Computer Science Tennessee State University Nashville, TN, USA · SAP AI Research and Innovation SAP Labs Palo Alto, CA, USA
Project Auto-World: Towards Automated Benchmarking of Neural Relational Reasoners
Jun 23, 2026Anirban Das, Joanne Boisson, Irtaza Khalid +2Relational Reasoning Synthetic Benchmark Generation
Cardiff University · University of Pennsylvania
Universal Encoders for Modular Relational Deep Learning
Jun 19, 2026Jakub Peleška, Gustav ŠírRelational Foundation Models Tabular Foundation Models
Czech Technical University in Prague, Karlovo náměstí 13, Prague, 121 35, Czechia
Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data
Jun 10, 2026Arie Soeteman, Balder ten Cate, Maurice Funk +3Graph Neural Networks Relational Reasoning
ILLC, University of Amsterdam, Amsterdam, Netherlands · Leipzig University, Leipzig, Germany · ScaDS.AI Center Dresden/Leipzig, Germany +2
What Makes a Desired Graph for Relational Deep Learning?
Jun 7, 2026Yao Cheng, Siqiang LuoGraph Structure Learning Graph Neural Networks
Nanyang Technological University, College of Computing and Data Science, Singapore.
HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning
Jun 4, 2026Moshiur Farazi, Sameera Ramasinghe, Mahbub Ahmed Turza +1Visual Reasoning VLM Reasoning
Data Science and AI, University of Doha for Science and Technology, Qatar · Pluralis Research, Australia · Department of Electrical and Computer Engineering, North South University, Bangladesh

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Revisiting Simple Baselines for Relational Deep Learning

Viet The Nguyen, Vijay Prakash Dwivedi, Gunther Gust

University of Würzburg · Seamless Labs, Inc. · Stanford University

Relational Deep Learning

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Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as temporal heterogeneous graphs. Recent advances based on graph neural networks and Transformers have achieved strong performance across diverse domains. However, the effectiveness of simpler relational baselines remains underexplored, making it difficult to assess how much architectural complexity is needed to achieve strong predictive performance. Motivated by the observation that predictive signals arise from target attributes, relational attributes, and historical labels, we revisit how effectively these signals can be exploited using simple propagation methods. We adapt fixed low-pass feature propagation to relational entity graphs and train an MLP for prediction. The resulting predictions are further refined using Correct-and-Smooth, a graph-based label propagation algorithm that incorporates historical labels over task-specific meta-graphs connecting target entities. Across classification and regression tasks on RelBench, an MLP trained on neighborhood-filtered features outperforms one using only target-entity features on most tasks and recovers a substantial portion of the performance of heterogeneous GNNs. Correct-and-Smooth provides further gains from historical labels, although applying it requires a separately constructed meta-graph because connections among target entities are not directly specified by the relational entity graph. Together, our studies revisit the effectiveness of established propagation methods as simple RDL baselines while identifying canonical meta-graph construction as a key challenge for effective label propagation.