arXiv:2609.37989v1 Announce Type: new
Abstract: Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, ta...
By Deqing Fu, Huangyuan Su, Rajat Sen, Taman Narayan, Sujay Sanghavi, Abhimanyu Das, Weihao Kong
arXiv:2603. 03589v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated.
By Arnab Phani, Elias Strauss, Sebastian Schelter
SAGE-Loop is a new closed‑loop, self‑adaptive AutoML framework that uses large language models to generate and validate machine learning pipelines in multiple rounds, allowing trial‑and‑repair and adaptive ensemble selection for both supervised and unsupervised tasks. It addresses the lack of instant feedback and correction in existing AutoML by enabling process‑level recovery from failures and dynamic use of model diversity. Experiments on 20 public datasets show consistent improvements in performance and stability across classification, regression, and clustering, and demonstrate the system’s ability to recover from execution failures.
By Junquan Gu, Shibo Cui, Xiangfeng Luo, Hang Yu
arXiv:2609.36970v1 Announce Type: new
Abstract: The Rashomon effect describes the existence of multiple near-optimal models that achieve comparable performance while offering fundamentally different...
By Katarzyna Wo\'znica, Katarzyna Rogalska, Zuzanna Sie\'nko, Mustafa Cavus
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
arXiv:2604. 18543v4 Announce Type: replace Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale.
By Xirui Li, Ming Li, Ion Stoica, Cho-Jui Hsieh, Tianyi Zhou