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Stack Attention (wip)

For following a line of research that augments attention with a differentiable stack, beginning with DuSell et al. at ETH Zurich

Citations

@misc{dusell2024stackattentionimprovingability,
    title   = {Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns}, 
    author  = {Brian DuSell and David Chiang},
    year    = {2024},
    eprint  = {2310.01749},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL},
    url     = {https://arxiv.org/abs/2310.01749}, 
}
@misc{zhang2025stacktranslargelanguagemodel,
    title    = {StackTrans: From Large Language Model to Large Pushdown Automata Model}, 
    author   = {Kechi Zhang and Ge Li and Jia Li and Huangzhao Zhang and Yihong Dong and Jia Li and Jingjing Xu and Zhi Jin},
    year     = {2025},
    eprint   = {2507.15343},
    archivePrefix = {arXiv},
    primaryClass = {cs.SE},
    url      = {https://arxiv.org/abs/2507.15343}, 
}

Release files for stack-attention-pytorch 0.0.1

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0.0.8

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0.0.3

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