Title: MambaSL: Exploring Single-Layer Mamba for Time Series Classification
Primary area: learning on time series and dynamical systems
Keywords: modular selective SSM, multi-head adaptive pooling, skip connection, single-layer Mamba, time series classification

Abstract: Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. 
We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific hypotheses. 
To address benchmarking limitations—restricted configurations, partial University of East Anglia (UEA) dataset coverage, and insufficiently reproducible setups—we re-evaluate 20 strong baselines across all 30 UEA datasets under a unified protocol. 
Our results show that MambaSL achieves state-of-the-art performance on the UEA benchmark among 21 models, with statistically significant average improvements over baselines while ensuring reproducibility via public checkpoints.

TL;DR: We introduce MambaSL, a minimally redesigned single-layer Mamba that achieves state-of-the-art accuracy on the UEA30 benchmark, with reproducible evaluation covering all baselines.