让AI诊断过程可解释，提升故障分析的可信度。
JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis
- 构建显式推理流程，追踪证据、假设与矛盾点
- 在66个真实故障中，诊断过程质量显著提升
- 适合需要可问责性分析的高风险系统运维
大型语言模型能生成流畅的根因分析，但在高风险操作中，仅靠流畅结论不足以保证责任可追溯。实际故障响应中，工程师需要了解诊断所依据的证据、考虑过的替代假设、存在的矛盾之处，以及系统是否已解决疑问。我们提出 JustDiag，一种用于根因分析（RCA）的诊断论证引擎，通过显式维护证据、发现、竞争假设、冲突和下一步检查等过程状态来填补这一空白。我们在66个真实事件上采用双层评估协议，分别评分最终答案质量和过程质量。相比无诊断论证的对照组，JustDiag 在结果与过程评分上均表现更优，虽终端完成率略低，但因更合理的不确定性保留而更具可信度。结果表明，可问责的根因分析需依赖显式的诊断论证产物与过程感知的评估，而非仅追求流畅的最终答案。
原文摘要 · Abstract (English)
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.
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