# LogReasoner: Empowering LLMs with Expert-like Coarse-to-Fine Reasoning for Automated Log Analysis

arXiv: https://arxiv.org/abs/2509.20798 · HTML: https://mlsanity.com/paper/2509.20798/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [LogLLM: Log-based Anomaly Detection Using Large Language Models](https://mlsanity.com/paper/2411.08561/) (2024)
- [LogLM: From Task-based to Instruction-based Automated Log Analysis](https://mlsanity.com/paper/2410.09352/) (2024)
- [LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis](https://mlsanity.com/paper/2407.01896/) (2024)
- [AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model](https://mlsanity.com/paper/2501.11031/) (2025)
- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)

## Built on by (most cited first, 2 of 2)

- [CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment](https://mlsanity.com/paper/2605.06702/) (2026)
- [SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data](https://mlsanity.com/paper/2610.16412/) (2026)

## Abstract (arXiv)

Log analysis is crucial for monitoring system health and diagnosing failures in complex systems. Recent advances in large language models (LLMs) offer new opportunities for automated log analysis, leveraging their reasoning capabilities to perform tasks such as anomaly detection and failure prediction. However, general-purpose LLMs struggle to formulate structured reasoning workflows that align with expert cognition and deliver precise details of reasoning steps. To address these challenges, we propose LogReasoner, a coarse-to-fine reasoning enhancement framework designed to enable LLMs to reason log analysis tasks like experts. LogReasoner consists of two stages: (1) coarse-grained enhancement of expert thinking, where high-level expert thoughts are constructed from collected troubleshooting flowcharts and existing tasks to enable LLMs to formulate structured reasoning workflows and (2) fine-grained enhancement of specific steps, where we first fine-tune the LLM with task-specific stepwise solutions to enhance the LLM for instantiated reasoning, then employ the preference learning to calibrate the LLM's reasoning details from its mistakes, further strengthen the LLM's analytical granularity and correctness. We evaluate LogReasoner on four distinct log analysis tasks using open-source LLMs such as Qwen-2.5 and Llama-3. Experimental results show that LogReasoner significantly outperforms existing LLMs, achieving state-of-the-art performance and demonstrating its effectiveness in enhancing the reasoning capabilities of LLMs for log analysis.