Abstract: We give the first fully dynamic algorithm which maintains a (1−є)-approximate densest subgraph in worst-case time poly(logn, є−1) per update. Dense subgraph discovery is an important primitive for many real-world applications such as community detection, link spam detection, distance query indexing, and computational biology. We approach the densest subgraph problem by framing its dual as a graph orientation problem, which we solve using an augmenting path-like adjustment technique. Our result improves upon the previous best approximation factor of (1/4 − є) for fully dynamic densest subgraph [Bhattacharya et. al., STOC ‘15]. We also extend our techniques to solving the problem on vertex-weighted graphs with similar runtimes.