The paper investigates a new failure mode in relational in-context learning called support‑set target leakage, where target‑derived features appear only in the labeled support set and not in the query. The authors create 14 synthetic leaker types across 20 columns, evaluate a frozen relational encoder on RelBench databases, and use Integrated Gradients to identify and mitigate the most harmful leakers. Results show that target‑table leakers degrade performance most, while one‑ and two‑hop leakers are less consistently exploited, and that IG can partially recover performance by removing high‑ranked leakers.
By Roshan Reddy Upendra, Alexandre Dorais, Joe Meyer, Andrew Pouret, Anastasios Lambrianos Stappas, Dinesh Katupputhur Ramprasath, Tom Palczewski, Minghua Li
arXiv:2606. 04320v1 Announce Type: cross Abstract: Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pass via relational in-context learning (ICL).
By Zhikai Chen, Junyu Yin, Jialiang Gu, Siheng Xiong, Xiaoze Liu, Ruowang Zhang, Keren Zhou, Kai Guo
arXiv:2602. 13697v2 Announce Type: replace-cross Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes.
By Linjie Xu, Yanlin Zhang, Quan Gan, Minjie Wang, David Wipf
arXiv:2609.20842v1 Announce Type: new
Abstract: Text-to-SQL translates natural-language questions into executable SQL queries, but open-source large language models still require task-specific post-t...
By Qifeng Cai, Xuanguang Pan, Hao Liang, Chang Xu, Wentao Zhang
The paper introduces RefineICL, an attention‑gated, feed‑forward‑network‑free framework that refines representations in situ for tabular foundation models. By using support labels to guide episode‑specific updates, the method transfers learned corrections to unlabeled queries without altering model parameters, achieving state‑of‑the‑art performance on AMLB29 and TabArena benchmarks. Experiments and internal interventions demonstrate that intermediate support updates are essential for constructing task‑specific predictors in context.
By Tian Zhou, Beverly Jin, Linxiao Yang, Xue Wang, Wenwei Wang, Bingqing Peng, Mengni Ye, Jinjie Gu, Liang Sun
GEAR is a two‑stage framework that distills tabular foundation models into lightweight MLP or tree‑based predictors for efficient CPU deployment. In the first stage, synthetic covariates are used as teacher‑query locations to train the student on soft TFM targets, expanding coverage beyond observed rows. The second stage re‑anchors the student to the target distribution using real labels and out‑of‑fold teacher predictions, preventing self‑labeling leakage and improving performance. Experiments on TALENT and TabArena show that GEAR‑distilled MLPs outperform supervised MLPs by up to 2.00 AUC points on binary tasks and 1.35 on multiclass tasks, and also outperform CatBoost, while dramatically reducing inference time and memory usage.
By Qi Qin, Jiajie Zhu, Dali Chen, Yuzhao Zhang, Jia-Xing Han, Yu Su, Peng Zhang, Ying Yan, Yifan Sun
arXiv:2609.05955v1 Announce Type: new
Abstract: Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictio...
By Mingqi Yang, Zidong Guo, Jihui Yang, Wenming Zuo
The paper evaluates 17 paradigm-level configurations of in‑context learning (ICL) text‑to‑SQL pipelines across five common modules, measuring each module’s marginal accuracy contribution and cost for four different backbone models. It finds that execution‑feedback refinement consistently improves accuracy at low cost, while other modules only help under specific backbone conditions. The study also shows that investing in a more elaborate pipeline for a mid‑tier backbone can be more cost‑effective than upgrading to a high‑capability model with a lean pipeline, providing a tiered, cost‑aware guideline that generalizes to additional backbones.
By Jiayan Lin, Yujia Liu, Zijin Hong, Zheng Yuan, Yilin Xiao, Hao Chen, Qinggang Zhang, Xiao Huang, Feiran Huang
PEARL is a new framework for inductive knowledge graph completion that treats relational paths as context-conditioned reasoning signals. It builds a query‑specific contextual subgraph from the query entities’ neighborhoods and uses a large language model‑guided retriever to select semantically relevant paths. By constructing a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, and applying a dual‑view contrastive objective, PEARL adapts path embeddings to local and global structural evidence, achieving the best average Hits@10 on WN18RR, FB15k‑237, and NELL‑995.
By Yunchi Yang, Longlong Li, Cunquan Qu
STEER is a sampling method for relational foundation models that reduces inference cost by focusing on the most relevant tables for a prediction task. It uses a large language model to rank foreign‑key edges in the database schema into relevance tiers, then assigns traversal probabilities based on these tiers. Evaluated on three state‑of‑the‑art RFMs, STEER cuts inference context size by roughly 40% on average while preserving or improving accuracy.
By Abdalla Mohamed, Ashraf Aboulnaga
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
arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.
By Mohamed Aly Bouke