SCOPE: Tree-based Self-Correcting Online Log Parsing via Syntactic-Semantic Collaboration

• URL: http://arxiv.org/abs/2603.27247v1
• Date: Sat, 28 Mar 2026 11:45:08 GMT
• Title: SCOPE: Tree-based Self-Correcting Online Log Parsing via Syntactic-Semantic Collaboration
• Abstract summary: SCOPE is the first self-correcting online log parsing method.<n>It integrates the strengths of both and LLM-based paradigms.<n>It outperforms state-of-the-art methods in both accuracy and efficiency.
• Score: 3.338964978828878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 se mantic 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 match ing 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 match ing, 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 ac curacy, 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.

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