Neuro-Symbolic Rule Discovery: Empowering LLMs with Causality for Vehicle Diagnostics

Defining Boolean logic for vehicle fault detection of error patterns (EPs) is a manual, error-prone bottleneck process in automotive safety. Standard LLMs struggle to automate this task, as they prioritize semantic plausibility over logical necessity. We propose CAREP, a framework that empowers LLMs with causal discovery to extract strict diagnostic rules from noisy high-dimensional event sequences. Instead of relying on semantic correlations, CAREP provides the LLM with a grounded set of causal drivers (excitatory) and constraints (inhibitory). This enables the automated synthesis of accurate, human-readable rules alongside reasoning traces. On a real-world dataset of 29,100 unique codes, CAREP achieves superior rule reconstruction accuracy compared to standard RAG baselines.