The article discusses goals as cognitive states that combine with world knowledge to guide purposeful behavior, emphasizing their compositional nature and relation to rational action. It draws parallels between goal representations and the syntax‑semantics interface in linguistics and logic, highlighting questions about expressivity, design, and efficiency of different goal languages. The authors synthesize research on goal representation properties, propose a broader design space, and suggest that distinguishing form and meaning can clarify assumptions, inform cognition‑motivation interactions, and identify variation axes in goal conceptions.
By David M. Abel, Mark K. Ho
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
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. 05638v1 Announce Type: cross Abstract: Teams deploying large language models in business contexts need evaluation systems, yet most treat evaluation as static model selection: run benchmarks, rank models, deploy the winner.
By Kenneth Benavides, Josh Fleischer, Danti Chen
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
arXiv:2601. 21787v2 Announce Type: replace-cross Abstract: The creation of Business Process Model and Notation (BPMN) models is a complex and time-consuming task requiring both domain knowledge and proficiency in modeling conventions.
By Chantale Lauer, Peter Pfeiffer, Alexander Rombach, Nijat Mehdiyev
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:2606. 15291v1 Announce Type: new Abstract: Agentic AI opens new opportunities for automating Business Process (BP), enabling autonomous decision-making and dynamic adaptation.
By Mohammad Azarijafari, Luisa Mich, Michele Missikoff
The paper introduces a decision‑aware suffix prediction framework that combines neural suffix predictors with decision mining rules extracted from event logs. By incorporating case‑ and event‑level attributes into the prediction process, the approach improves accuracy for short prefixes and rare process variants. Experiments on multiple event logs demonstrate that the framework not only enhances prediction performance but also provides intrinsic interpretability through mined decision rules.
By Henryk Mustroph, Stefanie Rinderle-Ma
arXiv:2606. 14654v1 Announce Type: new Abstract: Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work.
By Gaurav Verma, Scott Counts
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