@inproceedings{yu-etal-2025-sheaf,
    title = "Sheaf Discovery with Joint Computation Graph Pruning and Flexible Granularity",
    author = "Yu, Lei  and
      Niu, Jingcheng  and
      Zhu, Zining  and
      Chen, Xi  and
      Penn, Gerald",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.446/",
    pages = "8833--8848",
    ISBN = "979-8-89176-332-6",
    abstract = "In this paper, we introduce DiscoGP, a novel framework for extracting self-contained modular units, or sheaves, within neural language models (LMs). Sheaves extend the concept of functional circuits, a unit widely explored in interpretability research, by considering not only subsets of edges in an LM{'}s computation graph but also the model{'}s weight parameters. Our framework identifies sheaves through a gradient-based pruning algorithm that operates on both of these in such a way that reduces the original LM to a sparse skeleton that preserves certain core capabilities. Experimental results demonstrate that, across a range of linguistic and reasoning tasks, DiscoGP extracts sheaves that preserve 93-100{\%} of a model{'}s performance on the identified task while comprising only 1-7{\%} of the original weights and connections. Furthermore, our analysis reveals that, compared to previously identified LM circuits, the sheaves discovered by DiscoGP exhibit superior modularity and functional fidelity. Extending our method to the neuron level also unveils novel insights into the inner workings of LLMs."
}