arXiv AI June 16, 2026 By Aina Vila Pons, Ioannis Tzachristas, Constantinos Antoniou

LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction

arXiv:2606. 15314v1 Announce Type: cross Abstract: Industrial retrofit planning depends on structured operational data rather than free text: planners must estimate whether a newly registered prototype will require a retrofit, which retrofit package it will need, and how long the work will take.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 26

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

arXiv:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.

By Daria Grushina, Kseniia Kuvshinova, Alina Kostromina, Aziz Temirkhanov, Mile Mitrovic, Dmitry Simakov

llms benchmarks

arXiv Machine Learning

Aug 4

Why Large Language Models Fail at Tabular Prediction

arXiv:2608. 02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads: predictive analytics over tabular data.

By Marta Garnelo, Wojciech M. Czarnecki

llms agents fine-tuning benchmarks

arXiv Machine Learning

Aug 12

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

arXiv:2608. 10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples.

By Xu Zhang, Chang Xu, Hui Sun, Nan Ma, Zijian Zhang, Peng Wang, Wei Wang, Li Zhao

llms fine-tuning benchmarks safety

arXiv Machine Learning

Aug 7

SkillTFM: Gated Skill Evolution for Training-Free Adaptation of Tabular Foundation Models

arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.

By Yi He, Zhengkang Guan, Anpeng Wu, Peng Cui, Fei Wu, Kun Kuang

agents fine-tuning

arXiv Machine Learning

5d ago

TabH2O: A Unified Foundation Model for Tabular Prediction

arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.

By Pascal Pfeiffer, Dmitry Gordeev, Mathias M\"uller, Laura Fink, Joan Salv\`a Soler, Mark Landry, Branden Murray, Marcos V. Conde, Sri Satish Ambati

llms benchmarks

arXiv AI

Jul 21

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair

benchmarks

Related stories

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

Why Large Language Models Fail at Tabular Prediction

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

SkillTFM: Gated Skill Evolution for Training-Free Adaptation of Tabular Foundation Models

TabH2O: A Unified Foundation Model for Tabular Prediction

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting