JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 17 Jun 2026.
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15 of 22 references.
References · 15
- 2023Let's Verify Step by Stepcited by 3,598
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
- 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
- 2019ERASER: A Benchmark to Evaluate Rationalized NLP Modelscited by 523
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
- 2023RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Modelscited by 62
- 2024Exploring LLM-based Agents for Root Cause Analysiscited by 60
- 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 55
- 2025Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysiscited by 32
- 2021Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systemscited by 30
3 more
- 2021Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systemscited by 26
- 2024mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecturecited by 26
- 2025ThinkFL: Self-Refining Failure Localization for Microservice Systems via Reinforcement Fine-Tuningcited by 17
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