Semantic Curriculum for Anomaly Detection: A Unified Language-Driven Meta-Optimization Framework

System logs are central to diagnosing failures and monitoring behavior in complex software systems. Yet their unstructured format, semantic variability, and distributional shifts across systems make anomaly detection particularly challenging. Traditional methods - based on templates, static features, or rule-based matching - struggle to generalize, especially in low-label or cross-system scenarios. Large language models (LLMs) offer strong semantic understanding and can infer latent behavioral patterns from raw log text, while meta-learning enables fast adaptation to new tasks with few labels. To harness these complementary strengths, we propose a novel meta-learning framework that formulates log anomaly detection as a language-guided task induction problem. Rather than using LLMs as predictors or encoders, we employ them as semantic agents that decompose log data into structured meta-tasks and inform the adaptation of the meta-learner to new system environments. This synergy enables accurate and transferable anomaly detection across heterogeneous system environments, without requiring system-specific heuristics or extensive retraining. Experiments on diverse system log benchmarks demonstrate substantial gains in detection accuracy and robustness under distribution shift. To our knowledge, this is the first work to systematically integrate LLMs for task semantic modeling with meta-learning in system log anomaly detection, marking a shift toward generalizable, language-informed detection paradigms.