arXiv AI August 19, 2026 By Xuan Zheng, Kento Uchida, Shinichi Shirakawa

SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE

arXiv:2608. 17948v1 Announce Type: cross Abstract: Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions and trajectory-based prompting.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 24

PromptPack: Scaling LLM Annotation Agents for Online Recommendation

arXiv:2607. 20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives.

By Sebastian Koralewski, Merwan Barlier, Yulia Stolin, Bla\v{z} \v{S}krlj

llms agents efficiency 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 AI

Jun 11

ClawEnvKit: Automatic Environment Generation for Claw-Like Agents

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

agents benchmarks

arXiv AI

Jun 2

MiCU: End-to-End Smart Home Command Understanding with Large Language Model

arXiv:2606. 01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience.

By Haowei Han, Kexin Hu, Weiwei Cai, Debiao Zhang, Bin Qin, Yuxiang Wang, Jiawei Jiang, Xiao Yan, Bo Du

llms reinforcement-learning

arXiv AI

3d ago

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

arXiv:2608. 15592v1 Announce Type: new Abstract: Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput.

By Feiyang Ren, Shengtao Wen, Lingbing Guo, Yu Tian, Yuanning Cui, Xiang Chen

llms reinforcement-learning efficiency benchmarks

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

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Related stories

PromptPack: Scaling LLM Annotation Agents for Online Recommendation

TopoFE: topology-aware LLM-guided Automated Feature Engineering

ClawEnvKit: Automatic Environment Generation for Claw-Like Agents

MiCU: End-to-End Smart Home Command Understanding with Large Language Model

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

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