@inproceedings{beegamudre-etal-2026-token,
    title = "Token Pruning for Improving Graph-Generating State Space Model Performance",
    author = "Beegamudre, Monish  and
      Zheng, Jack  and
      Capetz, Margaret",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.35/",
    pages = "476--482",
    ISBN = "979-8-89176-383-8",
    abstract = "State Space Models (SSMs) have recently emerged as efficient alternatives to Transformers for sequence modeling, yet extending them to two-dimensional vision tasks remains challenging. The Graph-Generating State Space Model (GG-SSM) addresses this challenge by constructing an adaptive graph, achieving competitive performance on vision benchmarks. However, state propagation over the resulting graph introduces substantial inference overhead, limiting scalability to high-resolution inputs. In this work, we introduce a leaf-guided computation pruning strategy that accelerates GG-SSM inference without modifying the underlying graph topology. Rather than removing nodes or edges, our approach selectively scales or bypasses secondary refinement computations associated with high-dissimilarity leaf nodes, while preserving the low-weight MST backbone. Experiments on multiple long-term time series forecasting benchmarks demonstrate consistent throughput improvements with controlled accuracy degradation across a range of pruning ratios. These results indicate that structure-aware computation pruning is an effective mechanism for improving the scalability of graph-based state space models."
}