We present Lambda3, a novel zero-shot anomaly detection framework grounded in physical principles of structure tensors, topological invariants, and discrete structural jumps. Unlike conventional machine learning or statistical approaches, Lambda3 reframes anomaly detection as the identification of structural discontinuities and conservation law violations in evolving complex systems. Our method achieves universal, interpretable, and training-free detection of previously unseen anomalies by extracting physically meaningful features—including jump events, tension density, and topological charge—directly from multivariate time series data. To rigorously evaluate Lambda3's capabilities, we introduce a "Hell Mode" synthetic benchmark comprising eleven challenging physical anomaly patterns that overwhelm traditional detectors. Lambda3 consistently attains state-of-the-art performance (AUC > 0.93) across diverse, multi-modal, and correlated anomaly scenarios—all without access to historical or labeled data. In addition, every detected anomaly is accompanied by concrete structural, topological, and energetic explanations, enabling full interpretability and causal insight. Our efficient, JIT-compiled implementation allows real-time deployment in high-dimensional settings. These results demonstrate that physically-grounded, structure-based approaches can surpass black-box AI models, achieving robust, generalizable, and explainable anomaly detection. Lambda3 thus establishes a new paradigm for interpretable, universal intelligence in complex systems analysis.