arXiv AI

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.

arXiv AI

6d ago

LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs

arXiv:2608. 11220v1 Announce Type: new Abstract: Nowadays, the creation of a process flow diagram (PFD) and its subsequent transformation into a piping and instrumentation diagram (P&ID) is predominantly performed manually.

By Timur Zakarin, Sergei Voitov, Sergei Shumilin, Evgeny Burnaev

llms agents

arXiv Machine Learning

Jun 9

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.

By Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann

llms fine-tuning

arXiv AI

Jun 24

FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction

arXiv:2606. 24679v1 Announce Type: cross Abstract: Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators.

By Kunyu Ni, Lei Cao, Jie He, Xiaotong Zhang, Jianfeng Jin, Junyu Dong, Yanwei Yu

llms benchmarks data-engineering

arXiv AI

Jun 18

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.

By Fang Wang, Ernesto Damiani

llms benchmarks safety

arXiv Machine Learning

Jul 22

GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks

arXiv:2607. 19083v1 Announce Type: new Abstract: Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific tasks, outputs, and training regimes.

By Daniele Angioletti, Marco Nobile, Vittorio Limongelli

diffusion benchmarks

arXiv AI

Jun 9

Toward autocorrection of chemical process flowsheets using large language models

arXiv:2312. 02873v2 Announce Type: replace-cross Abstract: The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process flows and equipment configurations.

By Lukas Schulze Balhorn, Marc Caballero, Artur M. Schweidtmann

llms safety

arXiv Machine Learning

Jun 18

INDEQS: Informed Neural controlled Differential EQuationS

arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.

By Michael Detzel, Gabriel Nobis, Kristiyan Blagov, Juri Schubert, Jackie Ma, Wojciech Samek

benchmarks

arXiv Machine Learning

Aug 11

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models

arXiv:2608. 09031v1 Announce Type: new Abstract: Graph neural networks typically propagate information through repeated message-passing layers, coupling the distance over which information travels with the number of nonlinear transformations applied.

By Isuru Herath, Arin Gopakumar, Sharan Sahu

benchmarks

arXiv AI

Jun 9

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.

By Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu

llms diffusion reinforcement-learning

arXiv AI

Jul 28

Plain Transformers are Surprisingly Powerful Link Predictors

arXiv:2602. 01553v3 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies.

By Quang Truong, Yu Song, Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Jiliang Tang

llms rag benchmarks

arXiv Machine Learning

Aug 4

GraphIR: Architecture-Level Search States for LLM-Guided Neural Architecture Evolution

arXiv:2608. 01633v1 Announce Type: new Abstract: Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs.

By Zhen Liu, Wanqi Zhou, Shuanghao Bai, Yuhan Liu, Jinjun Wang, Jingwen Fu

llms benchmarks

arXiv Machine Learning

Jul 21

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

arXiv:2607. 16769v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems.

By Tushar Lone, Neha Karanjkar

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