arXiv:2608. 03413v1 Announce Type: new Abstract: As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems.
By Zuojun Max Shen, Yuan Qu, Pujun Zhang, Anbang Liu, Yunhao Liang
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
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:2607. 03228v1 Announce Type: new Abstract: LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems.
By Lukas Kirchdorfer, Adrian Rebmann, Christian Warmuth, Timotheus Kampik, Theiss Heilker, Gregor Berg
arXiv:2608. 08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work.
By Yijun Pan, Yukun Lian, Kunyu Shi, Junbo Li, Hongwei Xue, Sicong Xie, Guannan Zhang, Xiaoying Xing
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
arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.
By Zhihao Liu, Tianyu Wang, Xi Vincent Wang, Lihui Wang
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
By Arif Hassan Zidan, Yi Pan, Hanqi Jiang, Ruiyu Yan, Wei Ruan, Zihao Wu, Lifeng Chen, Weihang You, Xinliang Li, Bowen Chen, Huawen Hu, Peilong Wang, Sizhuang Liu, Jing Zhang, Siyuan Li, Zhengliang Liu, Yu Bao, Lin Zhao, Lichao Sun, Dajiang Zhu, Xiang Li, Jinglei Lv, Quanzheng Li, Wei Liu, Tianming Liu, Wei Zhang
arXiv:2606. 29799v1 Announce Type: new Abstract: This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis as a primary use case.
By Rafael Kaufmann, Felix Neub\"urger, Michael Walters, Thomas Kopinski, Dimitrije Markovi\'c
arXiv:2509. 26331v2 Announce Type: replace Abstract: The rapid advancement of LLMs sparked significant interest in their potential to augment or automate managerial functions.
By Berdymyrat Ovezmyradov
arXiv:2608. 06020v1 Announce Type: new Abstract: Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their beliefs and actions, and the market and institutional mechanisms through which their interactions produce aggregate outcomes.
By Jiale Han, Xiang Li, Jing Qian, Wenyuan Gu, Pin Gao, Ye Luo, Hongyuan Zha, Dacheng Tao, Benyou Wang, Lin William Cong
arXiv:2607. 03651v1 Announce Type: new Abstract: While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context.
By Xiaoyue Liu, Zheng Dong