arXiv:2606.14283v1 Announce Type: cross Abstract: Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring. However, standard objectives such as cross-entropy optimize local next-step likelihoods and only implicitly capture control-flow structure. As a result, models can achieve high token-level accuracy while permitting imprecise global behaviour. We introduce DIFF-ERO, a conformance-aware loss function for deep learning models on process data. DIFF-ERO is a differentiable formulation of entropy-based stochastic conformance that
The proliferation of deep learning in process analytics necessitates more precise methods to ensure models align with actual system behavior, moving beyond simple next-step predictions.
Improving conformance-aware loss functions is critical for deploying reliable AI systems in complex operational environments, ensuring their outputs are not just accurate but also logically consistent with process flows.
This development introduces a method for deep learning models in process mining to optimize for global process conformance, rather than just local accuracy, enhancing their applicability in real-world scenarios.
- · Process mining software providers
- · Companies implementing AI for operational efficiency
- · AI researchers in process analytics
- · Industries with complex operational processes (e.g., manufacturing, logistics)
- · Developers relying solely on traditional cross-entropy for process analytics
- · Systems with high token-level accuracy but poor global behavioral conformity
Deep learning models will more effectively capture and reproduce complex control-flow structures in process data.
This leads to more trustworthy and deployable AI systems for automation and optimization in enterprise processes.
Increased adoption of AI in critical operational roles, potentially accelerating 'lights-out' operations where human oversight is minimized.
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Read at arXiv cs.AI