[P] Comparing Mamba (SSM) vs. LSTM for Signal Recovery in Noisy Time Series

I’m a 2nd-year CS student experimenting with Selective State Space Models. I built a synthetic environment to see if Mamba’s selective scan actually helps ignore stochastic noise better than LSTM’s fixed gating in a high-entropy setting.

Key Finding: In an Out-of-Distribution ‘Stress Test’, the LSTM showed ‘Delusional Certainty’ (confidence -> 1.0) while Mamba stayed sceptical.

I’ve documented the Phase 1 results and the simulation logic here: jackdoesjava/mamba-ssm-microstructure-dynamics: Investigating the Information Bottleneck in Stochastic Microstructure: A Comparative Study of Selective State Space Models (Mamba) vs. Gated RNNs.

Would love any feedback on my implementation or thoughts on why the SSM is showing such better OOD resilience here.