arXiv AI June 9, 2026 By Ankai Hao, Ke Chen, Huan Li, Lidan Shou

LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)

arXiv:2606. 09004v1 Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE).

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

arXiv Machine Learning

Jun 2

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.

By Andrej Tschalzev, Nick Erickson, Yuyang Wang, Huzefa Rangwala, Stefan L\"udtke, Heiner Stuckenschmidt, Christian Bartelt

benchmarks

arXiv AI

Jul 28

TopoFE: topology-aware LLM-guided Automated Feature Engineering

arXiv:2607. 23286v1 Announce Type: new Abstract: Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large search space.

By Sha Li, Naren Ramakrishnan

llms benchmarks

arXiv Machine Learning

Aug 6

From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.

By Usha Shrestha, Dmitry Ignatov, Radu Timofte

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 30

Beyond IID: How General Are Tabular Foundation Models, Really?

arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.

By Lennart Purucker, Andrej Tschalzev, Nick Erickson, Gioia Blayer, David Holzm\"uller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Ga\"el Varoquaux, Frank Hutter

benchmarks

arXiv AI

Jun 2

BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution

arXiv:2606. 01286v1 Announce Type: cross Abstract: The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal.

By Yangzhen Wu, Aaron J. Li, Wenjie Ma, Li Cao, Ziheng Zhou, Mert Cemri, Shu Liu, Yuran Xiu, Chenxiao Yan, Haikun Zhao, Bin Yu, Ion Stoica, Dawn Song

llms reinforcement-learning benchmarks

arXiv AI

Jul 29

DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space

arXiv:2607. 25675v1 Announce Type: new Abstract: Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized artifacts remain inspectable and the model can be treated as a black box.

By Jiangwang Chen, Zixin Song, Junlin Liu, Shuaiyu Zhou, Haiyan Wu, Haihan Shi, Chenxi Zhou, Hanqing Li, Xiao Yang, Da Zhu, Guanjun Jiang, Hai Wan, Xibin Zhao

llms benchmarks

Related stories

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

TopoFE: topology-aware LLM-guided Automated Feature Engineering

From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

Beyond IID: How General Are Tabular Foundation Models, Really?

BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution

DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space