7 papers
Your Autoregressive Model Already Reveals the Causal Graph
Hugo Math, Rainer Lienhart
Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We exploit this observation to per…
Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences
Hugo Math
Electronic control units (ECUs) embedded within modern vehicles generate a large number of asynchronous events known as diagnostic trouble codes (DTCs). These discrete events form…
Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehicles
Hugo Math, Julian Lorenz, Stefan Oelsner +1
Modern vehicles generate thousands of different discrete events known as Diagnostic Trouble Codes (DTCs). Automotive manufacturers use Boolean combinations of these codes, called e…
Transforming Vehicle Diagnostics: A Multimodal Approach to Error Patterns Prediction
Hugo Math, Rainer Lienhart
Accurately diagnosing and predicting vehicle malfunctions is crucial for maintenance and safety in the automotive industry. While modern diagnostic systems primarily rely on sequen…
Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation
Hugo Math, Rainer Lienhart
Understanding causality in event sequences where outcome labels such as diseases or system failures arise from preceding events like symptoms or error codes is critical. Yet remain…
One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences
Hugo Math, Robin Schön, Rainer Lienhart
Understanding causality in event sequences with thousands of sparse event types is critical in domains such as healthcare, cybersecurity, or vehicle diagnostics, yet current method…