OpsMem: Dual-Memory Reasoning with Cross-Memory Resonance for Failure Diagnosis
arXiv:2607.11357
The paper presents OpsMem, a dual‑memory framework that combines a short‑term memory of the current diagnostic state with a long‑term memory of reusable operational experience, using cross‑memory resonance to improve iterative failure diagnosis in software systems.
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.
6 pages, 5 figures