@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension
%A Zhao, Hongbo
%A Wang, Huibin
%A Tang, Bin
%A Hu, Xianming
%A Huang, Yihong
%A Shen, Yijun
%A Chen, Nuoyi
%A Li, Ping
%A Zhang, Kai
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhao-etal-2026-adaptive
%X 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.
%U https://aclanthology.org/2026.findings-acl.1058/
%P 21067-21083
Markdown (Informal)
[Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension](https://aclanthology.org/2026.findings-acl.1058/) (Zhao et al., Findings 2026)
ACL
- Hongbo Zhao, Huibin Wang, Bin Tang, Xianming Hu, Yihong Huang, Yijun Shen, Nuoyi Chen, Ping Li, and Kai Zhang. 2026. Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension. In Findings of the Association for Computational Linguistics: ACL 2026, pages 21067–21083, San Diego, California, United States. Association for Computational Linguistics.