Log parsing is a critical step for automated log analysis in complex systems. Traditional heuristic-based methods offer high efficiency but are limited in accuracy due to overlooking semantic context. In contrast, recent LLM-based parsers improve accuracy via semantic understanding but incur high latency from frequent model calls. To address this, we propose SCOPE, the first self-correcting online log parsing method that integrates the strengths of both heuristic and LLM-based paradigms. SCOPE introduces a novel bi-directional tree structure that enables efficient template matching from both forward and reverse directions, resulting in a higher overall matching rate. Additionally, it adopts a two-stage syntactic-semantic collaboration framework: a lightweight NLP model first utilizes part-of-speech (POS) information for syntax-based matching, while the LLM is selectively invoked as a fallback to handle semantically complex cases when uncertainty remains. This design significantly reduces LLM API usage while maintaining high accuracy, achieving a balance between efficiency and effectiveness. Extensive evaluations on diverse benchmark datasets show that SCOPE outperforms state-of-the-art methods in both accuracy and efficiency. The implementation and datasets are publicly released to facilitate further research.

AIOps has become increasingly crucial in managing modern IT infrastructures, leveraging AI techniques to enhance operational efficiency and reliability in log anomaly detection. However, existing approaches, such as Deeplog, face two significant challenges in log anomaly detection: frequent changes in data patterns due to software and hardware upgrades, and the demand for high efficiency in online scenarios. To address these issues, we propose LogX, a novel method based on large language models and optimized prompting strategies, particularly interactive modes, to promptly correct previously unseen errors. By integrating input-label pairs directly into the prompt, LogX eliminates the need for iterative training processes and additional resource costs, ensuring high adaptability in online scenarios. Furthermore, to maintain control over sensitive data while ensuring privacy and security, we utilize open-source tools and on-premise infrastructure for AIOps system. It seamlessly integrates with LogX's online log diagnostic capabilities, providing a robust solution for companies aiming to manage their software maintenance processes internally.