@inproceedings{qi-etal-2026-scope,
    title = "{SCOPE}: Boosting {LLM} Efficiency with Scoped Position Encoding",
    author = "Qi, Qingguo  and
      Chen, Hongyang  and
      Li, Zhao",
    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.1650/",
    pages = "35653--35673",
    ISBN = "979-8-89176-390-6",
    abstract = "Positional encodings are fundamental to Transformers, yet explicit methods like RoPE can degrade under length extrapolation and may incur extra arithmetic and memory-access overhead. In this paper, we propose Scoped Position Encoding (ScoPE), a novel framework that reimagines structured sparsity as an intrinsic position encoding mechanism. Instead of relying on explicit arithmetic signals, ScoPE assigns exponentially scaled look-back scopes to attention heads. We theoretically demonstrate that this simple topological constraint transforms multi-head attention into a hierarchical processor, yielding an order awareness horizon that grows exponentially with depth up to the sequence length. Consequently, ScoPE is parameter-free and avoids relying on fragile positional arithmetic. Empirically, it significantly enhances efficiency by masking the majority of attention computations, offering a theoretical 8x reduction in attention FLOPs at long contexts. Extensive evaluations on LLaMA-3-8B architectures reveal that ScoPE achieves superior native length extrapolation and robust retrieval fidelity compared to RoPE, all while substantially reducing training and inference latency. The code is available at https://github.com/oncemoe/ScoPE."
}