State space models such as Mamba-3 claim improved state tracking over attention at subquadratic cost, but this claim is validated almost entirely on English. We test whether it transfers to a morphologically rich, low resource setting by training parameter and token matched Transformer, Mamba-3, and hybrid language models from scratch on Bangla (24.5M nonembedding parameters, 1B tokens, five seeds per architecture). We evaluate on a new suite of 4,790 native speaker reviewed minimal pairs targeting Bangla subject verb person and honorific register agreement, and assess every comparison with Wilson confidence intervals and cross seed Welch t-tests. Across five seeds, the Mamba family achieves significantly lower perplexity than the Transformer (Welch p < 0.001). Transformer SVA accuracy declines from 91.3% to 87.5% as the intervener lengthens (paired p=0.01), whereas Mamba-3 shows no significant decline (83.3% to 81.5%, p=0.22); however, the direct difference between their distance slopes is not significant (p=0.15). A third finding is methodological rather than architectural: most single run between architecture agreement gaps, including a honorific register advantage significant under a within run McNemar test (p = 0.0015) do not survive replication once cross seed variance is included (Welch p = 0.28), because Mamba-3’s agreement accuracy is markedly more seed sensitive than the Transformer’s (cross seed standard deviation up to ± 8.4 vs. ± 3.5). At this scale, two seed comparisons of state space models can manufacture findings that vanish under replication. Perplexity does not predict distance behavior: the architectural signal we identify concerns robustness to distance and initialization, not a uniformly “better” architecture. We release code, probes, all fifteen checkpoints, and the analysis scripts.