@inproceedings{zhao-etal-2026-adaptive,
    title = "Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free {LLM} Context Extension",
    author = "Zhao, Hongbo  and
      Wang, Huibin  and
      Tang, Bin  and
      Hu, Xianming  and
      Huang, Yihong  and
      Shen, Yijun  and
      Chen, Nuoyi  and
      Li, Ping  and
      Zhang, Kai",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1058/",
    pages = "21067--21083",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models exhibit degraded performance when extrapolating beyond training context lengths. Existing training-free methods like positional reuse or interpolation can alleviate this issue in an efficient manner. However, these strategies are semantics-agnostic by only considering relative token distances, which could indiscriminately blur semantically relevant and irrelevant tokens alike.To address this, we introduce an adaptive positional zooming method called **Relevance-Informed Positional Resource Allocation (RiPRA)**. RiPRA formulates positional encoding as a constrained resource allocation, in which a fixed positional budget is distributed across tokens in a longer context based on their semantic relevance to the query: relevant tokens get higher positional resolution, while irrelevant tokens (positions) are compressed. By doing this, RiPRA enables a dynamic and nonparametric positional zooming where the positional resolution is adaptively modulated across queries and network layers, effectively improving long-range context modeling and retrieval capacity. Besides, an isotonic smoothing is used to further enforce a global linear ordering relationship to preserve stability and generalization, together with a chunk-based hierarchical approximation to further reduce inference overhead. Extensive experiments across comprehensive benchmarks including LongBench, L-Eval, Passkey Retrieval, and PG19 demonstrate that RiPRA consistently outperforms existing training-free extrapolation methods, showing the value of relevance-conditioned positional encoding for long-context generalization."
}