LogNEO: GPT-Neo Framework Achieves High Performance in Real-Time Log Anomaly Detection
Researchers have introduced LogNEO, a log anomaly detection system built on a 1.3-billion-parameter GPT-Neo model fine-tuned with a novel reinforcement learning reward scheme. The system improves recall by up to 6 percentage points over the previous state-of-the-art method, LogGPT, across three standard benchmarks. It demonstrates practical deployment viability with 45-millisecond end-to-end latency at 15,000 events per second, addressing a key gap between research performance and production readiness.
LogNEO is a log anomaly detection framework presented by researchers at arXiv, leveraging EleutherAI's GPT-Neo large language model with 1.3 billion parameters. The system is fine-tuned using Proximal Policy Optimisation (PPO) with a custom partial-credit, exponentially decaying position-aware reward scheme combined with cross-entropy regularisation. This reward design explicitly accounts for prediction difficulty, granting higher rewards for correct early-position predictions and applying stronger penalties for late-position errors. LogNEO achieves F1-scores of 0.927, 0.913, and 0.984 on the widely used HDFS, BGL, and Thunderbird log benchmarks, respectively, outperforming LogGPT on recall while maintaining comparable precision. A production-oriented deployment was demonstrated using Apache Kafka for streaming, Redis for caching, and TensorRT-accelerated inference, achieving 45 ms end-to-end latency at a throughput of 15,000 events per second. The work addresses the growing need for reliable, real-time anomaly detection in large-scale computing infrastructure where log volumes can be enormous.
What's missing
The paper has not yet undergone peer review, as it is a preprint submitted to arXiv. Key open questions include how LogNEO performs on log datasets outside the three benchmarks tested, whether the position-aware reward scheme generalises to log formats with different structural properties, and what the computational and financial costs of deploying a 1.3B-parameter model are compared to lighter-weight alternatives. The paper does not report statistical significance or variance across multiple runs, which limits confidence in the benchmark comparisons.
What different sources said
- arXiv cs.AICenter
LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection
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