SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition

Online surgical phase recognition labels each frame of an operation from past frames alone. Because procedures run for hours, the cost of each prediction can- not grow with elapsed time. Structured state-space duality (SSD) meets that constraint, but is not well matched to surgical video in two ways. The same views recur through an operation, so a scene is written over its earlier occur- rence and afterwards only age separates the two. Phase durations differ by an order of magnitude, so a decay fast enough to clear a phase that passes in sec- onds erodes one that runs for minutes. The rate does vary from frame to frame, but on contexts this long it is not reliably learned into a forgetting schedule. State regramming applies an input-conditioned orthogonal rotation to the state handed across chunk boundaries, so where a frame is written also depends on what has passed since, and two occurrences of the same view are held apart when different phases intervene. Intensity-modulated stepping supervises a time- warp of the discretization step toward the annotated phase boundaries, clear- ing the state where a phase ends so that the decay within phases can lean to- ward retention. Both preserve SSD’s N -semiseparable structure and O(d) per- frame cost. On seven public benchmarks SurgicalMamba matches or exceeds the state of the art in online accuracy and phase-level Jaccard (94.6%/82.7% on Cholec80, 89.5%/68.9% on AutoLaparo) at 312.88 fps on a single GPU. Code :https://anonymous.4open.science/r/Surgical-Mamba-8930