@inproceedings{xiao-etal-2026-directions,
    title = "Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation",
    author = "Xiao, Xi  and
      Ma, Chenrui  and
      Zhang, Yunbei  and
      Liu, Chen  and
      Wang, Zhuxuanzi  and
      Li, Yanshu  and
      Zhao, Lin  and
      Hu, Guosheng  and
      Wang, Tianyang  and
      Xu, Hao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.97/",
    pages = "2132--2154",
    ISBN = "979-8-89176-390-6",
    abstract = "Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limitations: $\textit{semantic drift}$, arising from treating all update directions with equal importance, and $\textit{structural incoherence}$, due to adapting layers independently, resulting in uncoordinated and suboptimal updates. To address these issues, we propose $\textbf{StructLoRA}$, a framework that tackles both limitations through a principled dual-component design: (1) an Information Bottleneck-guided filter that prunes task-irrelevant directions to mitigate semantic drift, and (2) a lightweight, training-only graph-based coordinator that enforces inter-layer consistency to resolve structural incoherence. Extensive experiments across large language models, vision language models, and vision models (including LLaMA, LLaVA, and ViT) demonstrate that $\textbf{StructLoRA}$ consistently establishes a new state of the art, outperforming not only vanilla LoRA but also advanced dynamic rank allocation and sparsity-based methods. Notably, the gains are particularly pronounced in challenging low-rank and low-data regimes. Crucially, since the proposed modules operate only during training, $\textbf{StructLoRA}$ improves performance with $\textbf{zero additional inference cost}$, shifting the focus of PEFT from mere parameter compression to a more holistic optimization of information quality and structural integrity."
}