The paper introduces a scalable cross‑domain event extraction system built on a unified generative sequence‑to‑sequence framework. It jointly handles event detection and argument extraction, allowing both pipeline and end‑to‑end configurations. By fine‑tuning pretrained language models on multiple event datasets from diverse domains, the system retains domain‑specific semantics while generalizing across large, evolving label spaces, and offers a web‑based application for researchers to upload documents, extract events, visualize triggers and arguments, and compare configurations.
By Siting Liang, Omar Adjali, Omair Shahzad Bhatti, Daniel Sonntag
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
Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks.
arXiv:2605. 02488v2 Announce Type: replace Abstract: Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events.
By Periklis Mantenoglou
The paper introduces a unified multi-domain and multi-task generative framework for event extraction that incorporates explicit domain conditioning signals and task-specific prompts. This design allows a single model to adapt dynamically to different event schemas without needing full event label sets during inference, supporting both pipeline and end-to-end extraction. Experiments on various benchmarks show competitive performance, strong cross-domain generalization, and practical scalability while maintaining domain-specific precision.
By Siting Liang, Omar Adjali, Daniel Sonntag
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:2602. 06470v3 Announce Type: replace-cross Abstract: Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality data and diminishing returns from rising computational costs.
By Changyue Wang, Weihang Su, Qingyao Ai, Xingzhao Yue, Rui Zhang, Xiaojia Chang, Yiqun Liu
arXiv:2604. 22476v2 Announce Type: replace-cross Abstract: Disciplines such as business process management and process mining aid organizations by discovering insights about processes on the basis of recorded event data.
By Marco Pegoraro, Jonas Seng, Dustin Heller, Wil M. P. van der Aalst, Kristian Kersting
The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.
By Hugo Math
arXiv:2606. 09892v1 Announce Type: new Abstract: Textual event records, such as alarm logs, have become an increasingly common data source in engineering and manufacturing systems.
By Xiaofeng Xiao, Jianhong Chen, Qiuzhuang Sun, Naichen Shi, Xubo Yue
arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.
By Zhaoyan Sun, Shan Zhong, Daizhou Wen, Jiaxing Han, Guoliang Li, Ying Yan, Peng Zhang, Yu Su, Xiang Qi, Baolin Sun, Chengyuan Yang, Tao Fang, Huaiyu Ruan
arXiv:2510. 01427v3 Announce Type: replace Abstract: At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user instructions.
By Sipeng Zhang, Shuhuai Lin, Xinpeng Wei, Yihang Chen, Pin Qian, Su Wang, Huan Xu