@article{yang-etal-2026-simulating,
    title = "Simulating Hard Attention Using Soft Attention",
    author = "Yang, Andy  and
      Strobl, Lena  and
      Chiang, David  and
      Angluin, Dana",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "14",
    year = "2026",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.tacl-1.8/",
    doi = "10.1162/tacl.a.597",
    pages = "147--166",
    abstract = "We study conditions under which transformers using soft attention can simulate hard attention, that is, effectively focus all attention on a subset of positions. First, we examine several subclasses of languages recognized by hard-attention transformers, which can be defined in variants of linear temporal logic. We demonstrate how soft-attention transformers can compute formulas of these logics using unbounded positional embeddings or temperature scaling. Second, we demonstrate how temperature scaling allows softmax transformers to simulate general hard-attention transformers, using a temperature that depends on the minimum gap between the maximum attention scores and other attention scores."
}