​Epileptic seizures are often described as transitions into hypersynchrony, disorder or increased entropy. Prior neurotopological work argued that epileptic pathology may also be interpreted as breakdown of network geometry rather than entropy increase alone (Salazar Chon & Kadhim, 2025). Here, I report an expanded exploratory reconstruction of transition structure in a public HUP intracranial EEG cohort. The analysis used 85 derived ictal runs from 22 subjects and expanded the interictal control layer to 42 interictal pseudo-onset runs from 21 subjects. Markers were organized into algorithm-defined transition families, temporal first-crossing sequences, circular time-shift null controls and expanded ictal-versus-interictal screening. Across the expanded control, ictal runs showed stronger transition-risk escalation than interictal pseudo-onset segments at run level, subject level and matched-subject level. Early risk gain was higher in ictal runs at run level (median 0.4573 versus -0.0932, p<0.0001), subject level (median difference 0.3839, p=0.00008) and matched-subject level (paired median difference 0.3922, p=0.0046). In contrast, early latent radius/energy displacement was significant at run level but not robust at subject level, and latent-first ordering was not specific to ictal runs in the expanded control. These findings do not establish latent-state displacement as a validated seizure biomarker. They support a bounded hypothesis: derived iEEG seizure transitions can be reconstructed as heterogeneous algorithm-defined trajectories, with stronger ictal transition-risk escalation than interictal pseudo-onset controls, while latent-state displacement remains a candidate early transition marker requiring prospective validation.