  We devise an autoencoder based strategy to facilitate anomaly detection for
boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome
known limitations of GNN autoencoders, we design a symmetric decoder capable of
simultaneously reconstructing edge features and node features. Focusing on
latent space based discriminators, we find that such setups provide a promising
avenue to isolate new physics and competing SM signatures from
sensitivity-limiting QCD jet contributions. We demonstrate the flexibility and
broad applicability of this approach using examples of $W$ bosons, top quarks,
and exotic hadronically-decaying exotic scalar bosons.
