LogMoE: Lightweight Expert Mixture for Cross-System Log Anomaly Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Robust anomaly detection in system logs plays a crucial role in maintaining stable and reliable software operations. However, existing methods often struggle to accommodate evolving log formats and distributional shifts across systems, as they heavily rely on large volumes of labeled data, log parsing, and predefined event templates. To address these challenges, we propose LogMoE, a scalable and parsing-free log anomaly detection framework. LogMoE utilizes labeled logs from multiple mature systems to train a set of lightweight expert models, which are integrated via a gating mechanism within a Mixture-of-Experts (MoE) architecture. This design enables LogMoE to generalize effectively to previously unseen target systems. By eliminating the need for log parsing, our approach remains robust against the heterogeneity of log formats and syntactic structures. We conduct extensive evaluations on eight log datasets under varying generalization scenarios: single-system, homogeneous-system, and heterogeneous-system. Experimental results demonstrate that LogMoE consistently achieves robust generalization, particularly under conditions with scarce labeled data in the target system. As such, LogMoE provides a scalable, parsing-free, and generalization-capable solution tailored for complex and continuously evolving software system environments, positioning it as a future-ready approach to log anomaly detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it