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arXiv · 2607.11357

OpsMem: Dual-Memory Reasoning with Cross-Memory Resonance for Failure Diagnosis

Yongqian Sun·Rongchen Gao·Yu Luo·Wenwei Gu·Shenglin Zhang·Qingyi Guo·Qiuai Fu·Yaoliang Wu·Dan Pei
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Abstract

Failure diagnosis in modern software systems requires iterative evidence acquisition and hypothesis reasoning guided by operational experience. Existing LLM-based methods improve diagnosis through agentic reasoning or knowledge augmentation, but they often lack a mechanism to coordinate the evolving diagnostic state with operational experience during iterative diagnosis. We propose OpsMem, a dual-memory framework that maintains a short-term memory for the current diagnostic state and a long-term memory for reusable operational experience. OpsMem uses cross-memory resonance to activate state-relevant long-term memory, conditions multi-agent diagnosis on the short-term and activated long-term memories, and consolidates reusable experience from solved incidents back into long-term memory. Experiments on a real-world Huawei microservice failure diagnosis dataset show that OpsMem outperforms representative agentic-reasoning and knowledge-augmented baselines, improving Match and Relevant by up to 46.88% and 18.39% over the strongest baseline, respectively.

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Yongqian Sun, Rongchen Gao, Yu Luo, Wenwei Gu, Shenglin Zhang, Qingyi Guo, Qiuai Fu, Yaoliang Wu, Dan Pei. 2026-07-13. OpsMem: Dual-Memory Reasoning with Cross-Memory Resonance for Failure Diagnosis. https://arxiv.org/abs/2607.11357

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