PetriBench is a compact, fully self‑contained, and scalable benchmark that evaluates large language model (LLM) reasoning over dynamic state spaces using Petri nets. It organizes reasoning into four task families with Easy, Medium, and Hard levels, each generated by increasing structural complexity and evaluated against exact ground truth. Experiments across proprietary and open‑weight models show that accuracy consistently drops with difficulty, revealing distinct task‑specific capability profiles, while test‑time compute and procedural generation affect performance differently across tasks.
By Pyrros Koussios, Benjamin J\"ager, John Hua Yao, Ajay Sridhar, Violet Xiang, Chenhao Li
arXiv:2607. 04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining.
By Viki Peeva, Wil M. P. van der Aalst
The paper introduces a new approach to process model forecasting that uses causal nets instead of traditional directly-follows graphs, enabling explicit representation of concurrency. It forecasts time series of relation and binding counts, reconstructs future process models with AND/XOR semantics, and evaluates them using a protocol that handles partial traces for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance close to re‑mined models and outperform static discovery baselines, though filtering infrequent bindings improves metrics at the cost of losing concurrent behavior.
By Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt
The paper examines how event data from hospital processes can obscure whether activities occur concurrently or sequentially. It shows that standard event‑log approaches, based on stochastic language, often fail to distinguish concurrency because any log can be explained by a model with no concurrent events. The authors argue that the key to resolving this ambiguity lies in the choice of what is recorded—such as precise start and end times or object‑centric ordering—rather than simply collecting more data.
The paper examines how event logs used in process mining can fail to reveal concurrent versus sequential activities, using a hospital example where blood tests and imaging may occur simultaneously or in alternating order. It demonstrates that standard stochastic language approaches only expose the assumptions of their discovery algorithms, often misrepresenting concurrency. The authors argue that the key to distinguishing concurrent behavior lies in the choice of recorded data—such as precise start and end times or object‑centric ordering—rather than simply increasing sample size.
By Antony R. Lee, Peter Ti\v{n}o, Iain B. Styles
arXiv:2604. 22455v2 Announce Type: replace Abstract: A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines its maximal behavioral boundaries.
By Anti Alman, Izack Cohen, Avigdor Gal, Fabrizio Maria Maggi, Marco Montali
arXiv:2606. 17666v1 Announce Type: cross Abstract: Process twins provide real-time representations of entire production processes.
By Yash Pulse, Yong-Bin Kang, Abhik Banerjee, Prem Prakash Jayaraman
The paper introduces a scalable framework that uses finetuned large language models (LLMs) to translate unstructured textual resources into structured event logs for process mining. By creating a new text-to-log dataset and finetuning LLMs on it, the authors demonstrate that the resulting models produce high‑fidelity event logs, outperforming few‑shot or zero‑shot prompting methods. This approach enables previously unused organizational data, such as incident tickets and manuals, to be incorporated into process mining workflows.
By Maximilian Seeth, Gabriel Marques Tavares, Daniel Schuster
Process mining (PM) provides a powerful framework for discovering and optimizing operational processes from event data. However, the efficacy of PM techniques is strictly predicated on the availabilit...
arXiv:2607. 21530v1 Announce Type: cross Abstract: Concurrent stateful library APIs expose behavior through evolving resource ownership, lifecycle states, and competing interleavings.
By Kaiwen Zhang, Guanjun Liu
arXiv:2609.07984v1 Announce Type: new
Abstract: Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime prediction...
By Yiyuan Yang, Zheshun Wu, Yong Chu, Zhenghua Chen, Zenglin Xu, Qingsong Wen
arXiv:2607. 16738v1 Announce Type: new Abstract: AI-Augmented Business Process Management Systems (ABPMS) enhance traditional BPMS by leveraging advanced AI techniques to define, execute, and monitor complex process structures.
By Paul Wittlinger, Giacomo Acitelli, Anti Alman, Fabrizio Maria Maggi, Andrea Marrella