# Mamba State-Space Model

Most ICU sepsis warning models answer only whether sepsis will occur, leaving clinicians unsure of the onset context and early course once an alarm sounds. SepsisFlow places hourly pre-onset risk, the patient state at onset and the 72-hour organ-support trajectory on one clock advancing hourly from ICU admission: before onset it uses only bedside-visible records without antimicrobial or culture orders, reaching a 24-hour AUROC of 0.822 on the MIMIC-IV internal test set, and its onset states form three contexts with 28-day in-hospital mortality of 17.1%, 15.1% and 2.2%.

Most ICU sepsis warning models answer only whether sepsis will occur, leaving clinicians unsure of the onset context and early course once an alarm sounds. SepsisFlow places hourly pre-onset risk, the patient state at onset and the 72-hour organ-support trajectory on one clock advancing hourly from ICU admission: before onset it uses only bedside-visible records without antimicrobial or culture orders, reaching a 24-hour AUROC of 0.822 on the MIMIC-IV internal test set, and its onset states form three contexts with 28-day in-hospital mortality of 17.1%, 15.1% and 2.2%.

Surface-code decoders have processed the full $d^2 \times R$ syndrome array regardless of how many errors occurred, yet at roughly 0.1% physical error rate fewer than 5% of entries fire. The Sparse Mamba Decoder (SMD) processes only the k active detection events, each described by a 13-dimensional feature, and runs a Mamba state-space backbone in O(k) time. Under SI1000 noise at $p=1.5\times10^{-3}$, a three-checkpoint ensemble lowers the d=7 logical error rate by 16% relative to MWPM, and the single model reaches ratios of 0.65 at d=3 and 0.51 at d=5.

Surface-code decoders have processed the full $d^2 \times R$ syndrome array regardless of how many errors occurred, yet at roughly 0.1% physical error rate fewer than 5% of entries fire. The Sparse Mamba Decoder (SMD) processes only the k active detection events, each described by a 13-dimensional feature, and runs a Mamba state-space backbone in O(k) time. Under SI1000 noise at $p=1.5\times10^{-3}$, a three-checkpoint ensemble lowers the d=7 logical error rate by 16% relative to MWPM, and the single model reaches ratios of 0.65 at d=3 and 0.51 at d=5.