Cover Image

Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Balef, Amir Rezaei, Koshil, Mykhailo, Eggensperger, Katharina
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Understanding Layer Contributions in Tabular In-Context Learning Models
by: Balef, Amir Rezaei, et al.
Published: (2025)

In-Context Decision Making for Optimizing Complex AutoML Pipelines
by: Balef, Amir Rezaei, et al.
Published: (2025)

Put CASH on Bandits: A Max K-Armed Problem for Automated Machine Learning
by: Balef, Amir Rezaei, et al.
Published: (2025)

Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025)

TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2025)

Membership Inference over Diffusion-models-based Synthetic Tabular Data
by: Cheng, Peini, et al.
Published: (2025)

Distilling Tabular Foundation Models for Structured Health Data
by: Tanna, Aditya, et al.
Published: (2026)

LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models
by: Ipas, Oroel, et al.
Published: (2026)

TabDPT: Scaling Tabular Foundation Models on Real Data
by: Ma, Junwei, et al.
Published: (2024)

In-context Learning of Evolving Data Streams with Tabular Foundational Models
by: Lourenço, Afonso, et al.
Published: (2025)

One Sample is Enough to Make Conformal Prediction Robust
by: Zargarbashi, Soroush H., et al.
Published: (2025)

Generalist Foundation Models Are Not Clinical Enough for Hospital Operations
by: Jiang, Lavender Y., et al.
Published: (2025)

Tabular Foundation Models Can Learn Association Rules
by: Karabulut, Erkan, et al.
Published: (2026)

Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap
by: Tanna, Aditya, et al.
Published: (2026)

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
by: Nguyen, Duong, et al.
Published: (2026)

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
by: Qu, Jingang, et al.
Published: (2025)

Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory
by: Dong, Songwei, et al.
Published: (2026)

Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
by: Bouadi, Mohamed, et al.
Published: (2026)

Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
by: Wang, Yingshuo, et al.
Published: (2026)

One Token Embedding Is Enough to Deadlock Your Large Reasoning Model
by: Zhang, Mohan, et al.
Published: (2025)

How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning
by: Zhai, Zhiyuan, et al.
Published: (2026)

Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
by: Landsgesell, Jonas, et al.
Published: (2026)

Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2026)

FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection
by: Shen, Yuchen, et al.
Published: (2024)

Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models
by: Gangwani, Ishaan, et al.
Published: (2025)

Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding
by: Klein, Tassilo, et al.
Published: (2025)

MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
by: Sinodinos, Dimitrios, et al.
Published: (2025)

Inference Optimization of Foundation Models on AI Accelerators
by: Park, Youngsuk, et al.
Published: (2024)

Multi-Layer Attention-Based Explainability via Transformers for Tabular Data
by: Gavito, Andrea Treviño, et al.
Published: (2023)

Disentangling Tabular Data Towards Better One-Class Anomaly Detection
by: Ye, Jianan, et al.
Published: (2024)

Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
by: Lomurno, Eugenio, et al.
Published: (2026)

Understanding Foundation Models: Are We Back in 1924?
by: Smeaton, Alan F.
Published: (2024)

Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
by: Ren, Weijieying, et al.
Published: (2025)

Diversified Scaling Inference in Time Series Foundation Models
by: Hua, Ruijin, et al.
Published: (2026)

OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
by: Jiang, Dihong, et al.
Published: (2026)

SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?
by: Zhuang, Haomin, et al.
Published: (2024)

When Less is Enough: Efficient Inference via Collaborative Reasoning
by: Chen, Yilei, et al.
Published: (2026)

Sequential Compression Layers for Efficient Federated Learning in Foundational Models
by: Mahla, Navyansh, et al.
Published: (2024)

When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited
by: Agrawal, Rishabh, et al.
Published: (2026)

Inverting Foundation Models of Brain Function with Simulation-Based Inference
by: Bracher, Niels, et al.
Published: (2026)