2026-09-28T11:51:12Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27828 2026-09-28T06:35:13Z ddc:004 open_access

Using SLMs and LLMs for Diagnosis Plank, Sophia Wotawa, Franz LLM diagnosis performance SLM diagnosis performance comparative study Large Language Models (LLMs) have rapidly transformed how organizations approach complex problem solving, raising the question of whether they can also be applied to technical reasoning tasks. In this paper, we investigate whether LLMs can be used for system diagnosis, i.e., the localization of faults in systems comprising interacting components. In particular, we conducted an initial study using examples ranging from electric circuits to heating systems, evaluating 13 different language models of varying sizes with respect to the correctness and completeness of their diagnosis. The experimental evaluation revealed that language models with more parameters achieve superior performance. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sophia Plank and Franz Wotawa 2026 Is Part Of OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2026.14 urn:nbn:de:0030-drops-278284 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.14 eng https://creativecommons.org/licenses/by/4.0/legalcode