GlycanStructBench: Benchmarking Structure-Faithful Glycan Representations with Linkage-Aware Rooted Path Transport

Glycans pose a distinctive challenge for representation learning because their functions arise from branched architectures, not residue composition alone. Identical residue counts can differ in linkage, stereochemistry, root orientation, and terminal organization, so composition does not determine structure. This raises a central question: do models capture structural information or exploit compositional shortcuts? We introduce GlycanStructBench, a normalized-key-disjoint benchmark for evaluating whether glycan representations preserve structural distinctions across fifteen structural, taxonomic, and biological tasks. Its identity unit is the normalized structure key, not chemical identity. The benchmark includes composition-disjoint and motif-profile/cluster-disjoint stress-test splits, which withhold residue-count groups and whole clusters, respectively, and controlled probes of topology, linkage, and root-dependent processing. We also evaluate GlycoTransportFormer as a representative rooted-tree encoder with linkage-conditioned common-root-frame transport, not as a claim that this operator is optimal. Across seven models and five seeds, the encoder leads the structural and taxonomy families on the normalized-key and composition-disjoint partitions, with paired gains of 1.28 and 8.71 points, and 0.91 and 4.15 points under composition shift; all five paired seed-level differences are positive. On the motif-profile/cluster-disjoint partition it leads on subtype, type, and all eight taxonomy ranks. We report family-level paired effects and cross-partition consistency rather than a single mean rank. A parameter-free retrieval baseline exceeds the encoder on the two sparse biological audit tasks. These results show why glycan benchmarks should control for composition when assessing structure-faithful representations.