arXiv AI July 22, 2026 By Saleh Valizadeh Sotubadi, Nazanin Mahjourian, Vinh Nguyen

Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling

arXiv:2607. 18515v1 Announce Type: cross Abstract: This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets.

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

arXiv AI

Jun 17

TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins

arXiv:2606. 17660v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and na\"ive runs can even degrade model performance.

By Yuxiang Luo, Haonan Long, Chen Wang, Qiqi Duan, Xiaotian Lin, Yanwei Xu, Yuyu Luo, Weikai Yang, Nan Tang

llms fine-tuning data-engineering

arXiv Machine Learning

Jul 31

FunL2O: LLM-Guided Feature Function Design for Learning to Optimize

arXiv:2607. 27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance.

By Bingheng Li, Junyang Cai, Yupeng Zhang, Bistra Dilkina, Jayant Kalagnanam, Dzung T. Phan

llms

arXiv Machine Learning

Aug 7

When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters

arXiv:2608. 05207v1 Announce Type: new Abstract: Frozen pretrained forecasters often fail in structured, recurring ways that are costly to repair through fine-tuning.

By Fangxin Wang, Ziyi Zhang, Diyi Zhuang, Langzhou He, Shiyu Wang, Baichuan Mo, Philip S. Yu

llms agents fine-tuning

arXiv Machine Learning

6d ago

RelShap: Relationally Consistent Shapley Explanations

arXiv:2608. 11508v1 Announce Type: new Abstract: Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints.

By Seungeun Lee, Joao Fonseca, Julia Stoyanovich

arXiv Machine Learning

Jun 2

RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning

arXiv:2606. 01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures.

By Amanda S Barnard

arXiv Machine Learning

Jun 24

Machine Learning Modeling for Real-Time Melt Pool Monitoring in Laser Powder Bed Fusion Additive Manufacturing: A Hybrid Approach

arXiv:2606. 23851v1 Announce Type: new Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion (LPBF) additive manufacturing.

By Inioluwa Emmanuel, Zhuo Yang, Ho Yeung, Xinyao Zhang

rag computer-vision fine-tuning efficiency benchmarks

Related stories

TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins

FunL2O: LLM-Guided Feature Function Design for Learning to Optimize

When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters

RelShap: Relationally Consistent Shapley Explanations

RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning

Machine Learning Modeling for Real-Time Melt Pool Monitoring in Laser Powder Bed Fusion Additive Manufacturing: A Hybrid Approach