arXiv AI June 18, 2026 By Fang Wang, Ernesto Damiani

Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring

arXiv:2606. 18726v1 Announce Type: cross Abstract: Structurally constrained event sequence generation remains challenging because generated paths must preserve transition feasibility, temporal order, termination, and attribute consistency.

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

arXiv Machine Learning

Jun 19

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data

arXiv:2507. 22524v3 Announce Type: replace Abstract: We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction.

By Fang Wang, Paolo Ceravolo, Ernesto Damiani

arXiv AI

Jun 9

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

arXiv:2606. 08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting.

By Peiliang Gong, Emadeldeen Eldele, Chenyu Liu, Ziyu Jia, Yi Ding, Xinliang Zhou, Lianchao Gu, Qi Zhu, Yang Liu, Daoqiang Zhang, Xiaoli Li

llms benchmarks safety

arXiv AI

1d ago

Not All Attention Is Equal: A Quantitative Survey of the EEI Trade-off

arXiv:2608. 15459v1 Announce Type: cross Abstract: Attention mechanisms have driven machine learning for a decade, from neural machine translation to language models that do general-purpose reasoning.

By Aditya Singh

llms computer-vision nlp benchmarks safety

arXiv Machine Learning

Jun 16

David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

arXiv:2606. 15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering.

By Hans Weytjens, Ingo Weber

llms benchmarks

arXiv Machine Learning

Jun 3

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer

arXiv:2506. 00431v2 Announce Type: replace Abstract: Due to the proficiency of self-attention mechanisms (SAMs) in capturing dependencies in sequence modeling, several existing dynamic graph neural networks (DGNNs) utilize Transformer architectures with various encoding designs to capture sequential evolutions of dynamic graphs.

By Jie Peng, Zhewei Wei, Yuhang Ye

llms rag benchmarks safety

arXiv Machine Learning

Jul 31

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.

By Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn

llms benchmarks

Related stories

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

Not All Attention Is Equal: A Quantitative Survey of the EEI Trade-off

David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches