Log anomaly detection (LogAD) is crucial for identifying failures and threats in large-scale computing and cyber-infrastructure systems. However, most existing LogAD approaches suffer from key limitations: they depend on slow and error-prone log parsing, employ tightly coupled end-to-end pipelines, often require supervision for improved detection performance, and rely on flawed single-pass evaluation protocols that fail to reflect the temporal dynamics of real-world online detection. These issues significantly hinder scalability, adaptability, and the practical deployment of solutions. To address these limitations, we introduce $\mathbf{K}^{\mathbf{4}}$ (Knowing the Unknown by Knowing only the Known), a fully unsupervised, parser-independent, and representation-agnostic LogAD framework designed for high-performance online detection. At its core, $K^{4}$ is grounded in a novel formulation based on representation-level typicality estimation, which transforms arbitrary log embeddings into compact and interpretable four-dimensional descriptors: Precision, Recall, Density, and Coverage (PRDC), which are swiftly computed via GPU-acceleration into geometric $k$-nearest neighbor statistics. These descriptors inform lightweight, modular detectors, including KDE, GMM, OCSVM, and a new adaptation of DeepSVDD, which enables efficient and accurate anomaly scoring without relying on structured formats or log representation retraining. To support realistic deployment scenarios, we also propose a principled streaming-faithful evaluation protocol that partitions datasets into fixed-size chunks and applies sliding-window sampling with strides to emulate online log ingestion, alleviating the performance overestimation and dataset undercoverage issues of prior single-pass evaluations and enabling reproducible benchmarking across datasets with varying anomaly densities. Using this setup, we conduct over $\mathbf{1 2 5, 0 0 0}$ experiments across three real-world datasets (HDFS, BGL, Thunderbird), six pretrained embedding models, four detectors, and multiple training and log sampling configurations. Compared to six representative baseline methods spanning supervised, semi-supervised, self-supervised, and unsupervised paradigms, $K^{4}$ consistently sets new state-of-the-art results (AUROC: 0.995-0.999, F1: 0.989-0.992) and outperforms all baselines by large margins, while keeping detector training under 4 seconds and per-sample inference latency as low as $4 \mu ~\mathrm{s}$, which are orders of magnitude faster than the most competitive alternatives.