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A Simple State Space Model Excels at Multivariate Time Series Classification

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arXiv:2605. 27406v1 Announce Type: new Abstract: Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity.

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Authors: Hassan Saadatmand, Geoffrey I. Webb, Hamid Rezatofighi, Mahsa Salehi
Released: 2026-05-28 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because A Simple State Space Model Excels at Multivariate Time Series Classification overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, class, under, eval, line, project, position, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.27406

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