Imagine a world of intricate processes, like the journey of an online order from click to doorstep or the complex dance of a supply chain. Now, imagine trying to find the glitches, the unusual hiccups that disrupt these flows. That's the challenge of anomaly detection, and it's getting a powerful upgrade thanks to the world of object-centric process mining. Traditional methods often struggle to capture the full picture, missing the interconnectedness of different parts within a process. This new research tackles this head-on by looking at processes not as single streams, but as interacting objects – like individual orders, invoices, or products in a supply chain. Think of it as zooming in on each moving piece and understanding how they relate to the bigger picture. The researchers propose innovative techniques to pinpoint anomalies. They’ve developed a method using "oracles," leveraging domain expertise to identify unusual feature values. They also introduce an approach that scores the "anomalousness" of each object, allowing analysts to focus on the most unusual cases. A third method cleverly aggregates these scores to pinpoint specific features causing problems. Testing these methods on a real-world purchase-to-pay process revealed some surprising insights. They uncovered "maverick buying," where orders are placed without proper approvals, and "post-mortem changes" to purchase requisitions, masking discrepancies from managers. These are not just technical glitches, but real-world behaviors with significant financial impact. One of the most exciting aspects of this research is the exploration of Large Language Models (LLMs) as domain knowledge providers. Imagine having an AI assistant that can help interpret complex data, suggesting potential causes for anomalies. While promising, the research acknowledges the current limitations of LLMs, like inconsistencies and "hallucinations." Think of these powerful tools as apprentices, still learning the ropes but with enormous potential. This research isn't just about finding anomalies; it's about understanding *why* they occur. By linking object-centric features with anomaly detection algorithms, it offers a powerful new lens into the complex world of business processes, paving the way for more efficient, resilient, and transparent operations.