TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models

arXiv:2609.37989 · cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes

Sources

• MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery
• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
• TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
• TabPFN-3: Technical Report
• TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
• AIDE: AI-Driven Exploration in the Space of Code
• AlphaEvolve: A coding agent for scientific and algorithmic discovery
• Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
• Towards Pretraining Text Encoders for TabPFN
• Self-Improving Language Models with Bidirectional Evolutionary Search
• R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks