Think of this as a detailed troubleshooting decision tree for machines that an AI must follow and be graded on. The decision tree (a DAG) encodes expert fault-finding steps; the LLM tries to diagnose faults using sensor data and descriptions; the framework checks how well the AI followed the steps and reached the right cause, providing a rigorous way to test and improve AI-based maintenance assistants.
Provides a structured, engineering-grade way to evaluate whether an LLM-based maintenance assistant can correctly diagnose equipment faults and follow prescribed troubleshooting paths, instead of relying on ad-hoc or subjective assessments.
Domain-specific DAG representations of fault trees and evaluation metrics tailored to L-DED and similar industrial maintenance workflows.
Hybrid
Context Window Stuffing
High (Custom Models/Infra)
Building and maintaining accurate DAGs of fault trees and labeled evaluation scenarios; evaluation cost grows with number of scenarios and model variants tested.
Early Adopters
Uses directed acyclic graphs that mirror industrial fault trees as the backbone for evaluating LLM reasoning in diagnostics, going beyond simple question–answer accuracy tests or generic benchmark datasets.