DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules

• URL: http://arxiv.org/abs/2605.08614v1
• Date: Sat, 09 May 2026 02:17:39 GMT
• Title: DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules
• Abstract summary: Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions.<n> translating rules into maintenance steps requires asset-specific knowledge gained through years of practice.<n>We investigate whether LLMs can serve as decision support for this rule-to-action step and introduce ours, a benchmark of 6,690 expert-validated multiple-choice questions.
• Score: 4.124344125532972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perform corrective actions. The bottleneck is not detection but response: translating rules into maintenance steps requires asset-specific knowledge gained through years of practice. We investigate whether LLMs can serve as decision support for this rule-to-action step and introduce \ours{}, a benchmark of 6{,}690 expert-validated multiple-choice questions from 118 rule-action pairs across 16 asset types. We contribute (i) a symbolic-to-MCQA pipeline normalizing rules to Disjunctive Normal Form with embedding-based distractor sampling, (ii) five variants probing distinct failure modes (Pro, Pert, Verbose, Aug, Rationale), and (iii) a benchmark of 29 LLMs and 4 embedding baselines. A human evaluation (9 practitioners, mean 45.0\%) confirms \ours{} requires specialist knowledge beyond operational experience. Three findings stand out. The frontier has closed: the top three LLMs lie within one Macro point, with Bradley-Terry Elo placing claude-opus-4-6 30 points above the next model. Yet \ours{}\,Pro exposes brittleness, with every model losing 13--60\% relative accuracy under distractor expansion. \ours{}\,Aug exposes pattern-matching: under condition inversion, frontier models still select the original answer 49--63\% of the time. The deployment bottleneck is not capability but calibration: frontier models handle template-style fault detection but break under structural perturbation.

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