Source-grounded Fault Memory for Retrieval-augmented Diagnosis Agents
DOI:
https://doi.org/10.66238/cmcsmg66Keywords:
Retrieval-Augmented Generation, Fault Memory, Source Grounding, Diagnostic Agents, Automated TroubleshootingAbstract
The increasing complexity of modern technological systems, ranging from distributed computing architectures to advanced manufacturing equipment, necessitates robust automated diagnostic solutions. Large language models integrated with retrieval-augmented generation frameworks have emerged as promising tools for complex fault diagnosis. However, these systems frequently suffer from hallucination and temporal inconsistency, particularly when encountering rare or cascading failure modes. To address these critical limitations, this paper introduces a novel framework based on source-grounded fault memory for retrieval-augmented diagnosis agents. By anchoring the reasoning process in verifiable, domain-specific historical fault logs and systematically integrating an episodic memory module, the proposed architecture significantly enhances the accuracy, traceabilty, and reliability of diagnostic outputs. The fault memory acts as a dynamic repository of previously resolved anomalies, while the source-grounding mechanism guarantees that generated solutions are strictly constrained by factual documentation. Extensive theoretical analysis and simulated benchmarking across diverse industrial datasets demonstrate that the integration of source-grounded fault memory reduces diagnostic hallucination and improves first-time resolution rates compared to conventional retrieval-augmented baselines. This research provides a foundational step toward developing trustworthy, autonomous diagnostic agents capable of operating in high-stakes environments where epistemological certainty is paramount.
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Copyright (c) 2026 Alice Ross (Author)
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