Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data (2026.acl-industry)
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Fearghal O’Donncha, Nianjun Zhou, Natalia Martinez, James T Rayfield, Fenno F. Heath III, Abigail Langbridge, Roman Vaculin
| Challenge: | Existing maintenance systems do not support conditional reasoning, argues a new study . large language models (LLMs) offer flexible reasoning, but naively applying generative models introduces risks, he says . |
| Approach: | They propose a maintenance language-based reasoning framework that constrains reasoning through deterministic evidence construction and structured failure knowledge. |
| Outcome: | The proposed framework produces evidence-grounded explanations and advisory actions under heterogeneous data, a study shows . it constrains reasoning through deterministic evidence construction and structured failure knowledge, and applies a rule-based verification loop to suppress unsupported conclusions. |
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