arXiv AI

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.

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

arXiv AI

Jun 9

Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence

arXiv:2412. 00508v2 Announce Type: replace-cross Abstract: Control structure design is an important but tedious step in P&ID development.

By Lukas Schulze Balhorn, Kevin Degens, Artur M. Schweidtmann

arXiv Machine Learning

Aug 12

ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models

arXiv:2608. 10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what happens while treating when it happens as a secondary concern.

By Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji, Francesco Bronzino

llms

arXiv AI

Jun 9

Capacity-Controlled Global Attention for Graph Transformers

arXiv:2604. 17324v2 Announce Type: replace-cross Abstract: Global self-attention drives modern graph transformers, yet the softmax at its core imposes a structural constraint rarely examined directly: every attention row is non-negative and sums to one, so each per-head output is a mass-conserving convex combination of value vectors.

By Yang Liu, Dongxin Guo, Tom Zheng, Siu Ming Yiu, Liam Ning, Jikun Wu

llms benchmarks

arXiv AI

Jul 20

Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes

arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.

By Jaeyeong Lee, Taeseong Yoon, Wonmo Koo, Heeyoung Kim

arXiv AI

Jun 10

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

arXiv:2606. 10461v1 Announce Type: cross Abstract: Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics.

By Xianlin Zeng, Fan Xia, Xiangyu Chen

llms rag diffusion benchmarks safety

arXiv AI

Aug 10

LiFTER: A Grounded Neuro-Symbolic Microscope for Continuous-Time Dynamic Graph Forecasting

arXiv:2608. 06765v1 Announce Type: new Abstract: Continuous-time dynamic graph models predict future links by compressing past interactions into neural states.

By Minwoo Yu, Young-guk Ha

benchmarks

Related stories