Hugging Face Trending Papers July 13, 2026

STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling

Read the original on Hugging Face Trending Papers →

Career paths encode decades of skill acquisition, role transitions, and educational investment, and understanding them at scale underpins workforce planning, labor market policy, and job recommendation. Resumes are a rich source of information about career paths: they contain detailed descriptions of work experience, education, and skills.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

1d ago

Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment

arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.

By Yanru Sun, Emadeldeen Eldele, Zongxia Xie, Yucheng Wang, Wenzhe Niu, Qinghua Hu, Chee Keong Kwoh, Min Wu

llms benchmarks safety

arXiv AI

Aug 6

DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginations

arXiv:2604. 23054v2 Announce Type: replace-cross Abstract: Predicting the outcomes of prospective clinical trials remains a major challenge.

By Youze Zheng, Jianyou Wang, Yuhan Chen, Matthew Feng, Longtian Bao, Hanyuan Zhang, Maxim Khan, Aditya K. Sehgal, Christopher D. Rosin, Umber Dube, Ramamohan Paturi

llms

arXiv Machine Learning

Jul 2

StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting

arXiv:2607. 00197v1 Announce Type: new Abstract: Long-horizon multivariate time series forecasting (LTSF) remains challenging due to non-stationarity, regime shifts, and error accumulation.

By Haroon Gharwi, Yue Dai, Kai Shu

llms benchmarks

arXiv AI

Jun 30

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems

arXiv:2606. 28533v1 Announce Type: cross Abstract: Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuances of user behavior.

By Zikun Cui, Renzhi Wu, Junjie Yang, Li Sheng, Jijie Wei, Linfeng Liu, Tai Guo, Tao Jia, Xiaodong Wang, Hong Li, Li Yu, Sri Reddy, Hong Yan

llms diffusion benchmarks

arXiv Machine Learning

Jun 5

Pretraining Recurrent Networks without Recurrence

arXiv:2606. 06479v1 Announce Type: new Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations.

By Akarsh Kumar, Phillip Isola

llms

arXiv Machine Learning

Jul 14

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting

arXiv:2602. 03564v2 Announce Type: replace Abstract: Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dynamics.

By Mingyue Cheng, Yaguo Liu, Daoyu Wang, Xiaoyu Tao, Qi Liu

llms diffusion multimodal benchmarks

Related stories

Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment

DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginations

StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems

Pretraining Recurrent Networks without Recurrence

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting