@inproceedings{yao-etal-2026-par,
    title = "{PAR}: Training-Free Positional Perturbation and Attention Recycling for Faithful {OCR}",
    author = "Yao, Yao  and
      Liao, Manwen  and
      Zhang, Weitian  and
      Li, Zuchao  and
      Zhao, Hai",
    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.1065/",
    pages = "23258--23273",
    ISBN = "979-8-89176-390-6",
    abstract = "In high-precision scenarios, vision language models suffer from Linguistic Priors Hallucination. When processing familiar text, models tend to over-rely on internal parametric knowledge, effectively ``reciting'' the content rather than ``reading'' the image. In this paper, we first systematically investigate this phenomenon by constructing the GlitchText Probing Dataset. We discover that the model{'}s reliance on visual grounding diminishes significantly as the generation length increases. To mitigate this, we propose PAR (Positional Perturbation and Attention Recycling), a training-free, inference-time intervention framework. PAR consists of two parts: (1) Positional Perturbation (PP) injects structured phase noise into the rotary positional embeddings; (2) Foveal Attention Recycling (FAR) detects over-confident linguistic priors and dynamically redistributes attention mass back to important visual regions. Extensive experiments across state-of-the-art models, demonstrate that PAR significantly reduces hallucination rates (reducing CER by 12{\%}), particularly in long-context scenarios, while maintaining robust generalization on standard benchmarks."
}