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arXiv · 2601.13048

Analysis of Long Range Dependency Understanding in State Space Models

Srividya Ravikumar·Abhinav Anand·Shweta Verma·Mira Mezini
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Abstract

Although state-space models (SSMs) have demonstrated strong performance on long-sequence benchmarks, most research has emphasized predictive accuracy rather than interpretability. In this work, we present the first systematic kernel interpretability study of the diagonalized state-space model (S4D) trained on a real-world task (vulnerability detection in source code). Through time and frequency domain analysis of the S4D kernel, we show that the long-range modeling capability of S4D varies significantly under different model architectures, affecting model performance. For instance, we show that the depending on the architecture, S4D kernel can behave as low-pass, band-pass or high-pass filter. The insights from our analysis can guide future work in designing better S4D-based models.

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Srividya Ravikumar, Abhinav Anand, Shweta Verma, Mira Mezini. 2026-01-19. Analysis of Long Range Dependency Understanding in State Space Models. https://arxiv.org/abs/2601.13048

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