JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis

2026-06-17

AI Summary

This work addresses the limited interpretability and accountability of large language models (LLMs) in root cause analysis, which hinder their applicability in high-stakes operational settings requiring rigorous evidence chains, hypothesis comparison, and uncertainty handling. The authors propose JustDiag, a diagnostic argumentation engine that introduces, for the first time, an explicit modeling of the diagnostic reasoning process into root cause analysis. JustDiag structures and maintains states such as evidence, findings, competing hypotheses, conflicts, and follow-up checks to enable traceable and auditable inference, complemented by a calibration mechanism that explicitly accounts for uncertainty. Integrating LLMs with a structured reasoning framework, the approach employs a two-tier evaluation protocol to assess both outcome and reasoning quality. Experiments on 66 real-world incidents demonstrate that JustDiag significantly outperforms non-argumentative baselines in both outcome and process scores, exhibiting superior uncertainty retention despite a slightly lower completion rate.

Abstract

Large language models can produce fluent root cause analyses, but fluent final answers alone are insufficient evidence for accountability in high-stakes operations. In real incident response, engineers need to know what evidence supported a diagnosis, which alternatives were considered, where contradictions remained, and whether the system resolved the case or preserved uncertainty. We address this gap with JustDiag, a diagnostic justification engine for RCA that maintains an explicit process state over evidence, findings, competing hypotheses, conflicts, and next checks. We evaluated the system on 66 real-world incidents using a two-layer protocol that separately scores final-answer quality and process quality. Relative to a matched control without diagnostic justification, JustDiag achieved stronger outcome and process scores, while accepting slightly lower terminal completion due to more calibrated non-closure. These results suggest that accountable RCA requires explicit diagnostic justification artifacts and process-aware evaluation, not only fluent final answers.

Problem

• root cause analysis
• accountability
• diagnostic justification
• large language models
• incident response

Innovation

• diagnostic justification
• root cause analysis
• accountability
• process-aware evaluation
• large language models