The triangle detection data structures introduced in the paper have applications beyond feedback problems in tournaments. One potential extension could be in the field of social network analysis, where identifying triangles (cycles of length three) can reveal important structural patterns such as clustering and community detection. By leveraging the efficient triangle detection capabilities of these data structures, researchers could develop algorithms for community detection, identifying closely connected groups of nodes within a network. Additionally, the data structures could be applied to problems in computational biology, such as analyzing protein-protein interaction networks to identify functional modules or protein complexes based on triangle motifs. Expanding the use cases of these data structures to diverse fields can unlock new possibilities for graph analysis and optimization.