Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Model Dependence and Evaluation Reliability
cs.AI
Submitted: 2026-09-29
Updated: 2026-09-29
Terminology
Sources
- Griffin: Towards a Graph-Centric Relational Database Foundation Model
- Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
- Relational In-Context Learning via Synthetic Pre-training with Structural Prior
- OpenRFM: Dissecting Relational In-Context Learning
- KumoRFM-2: Scaling Foundation Models for Relational Learning
- PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
- The CTU Prague Relational Learning Repository
- RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
- SALT: Sales Autocompletion Linked Business Tables Dataset
- Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases
- Parameter-Free Encoders Remain Viable for RDB Foundation Models
- Task Scarcity and Label Leakage in Relational Transfer Learning
- ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
- Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
- A Mechanistic Study of Tabular Foundation Models
- TabICLv2: A better, faster, scalable, and open tabular foundation model
- GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
- VIP-COP: Context Optimization for Tabular Foundation Models
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