@inproceedings{hu-etal-2026-every,
title = "Every Token Counts: Generalizing 16{M} Ultra-Long Context in Large Language Models",
author = "Hu, Xiang and
Zhou, Zhanchao and
Liang, Ruiqi and
Li, Zehuan and
Wu, Wei and
Li, Jianguo",
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://aclanthology.org/2026.acl-long.464/",
pages = "10208--10220",
ISBN = "979-8-89176-390-6",
abstract = "This work explores efficient ultra-long context modeling. We posit that an effective solution requires three fundamental properties: sparsity, random-access flexibility, and \textbf{length generalization. To achieve this, we leverage Hierarchical Sparse Attention (HSA), a novel attention mechanism that satisfies all three properties. We integrate HSA into the Transformer architecture to develop HSA-UltraLong, an 8B-parameter Mixture-of-Experts (MoE) model trained on over 8 trillion tokens. We rigorously evaluate the model across tasks with both in-domain and out-of-domain context lengths to validate its capabilities. Our model demonstrates comparable performance to full-attention baselines on in-domain sequence lengths. Crucially, it achieves over 90{\%} accuracy on most in-context retrieval tasks with contexts up to 512 times the pre-training context length. This work reports our findings and remaining issues throughout the experiments, offering insights for future research in ultra-long context modeling.}"
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<abstract>This work explores efficient ultra-long context modeling. We posit that an effective solution requires three fundamental properties: sparsity, random-access flexibility, and length generalization. To achieve this, we leverage Hierarchical Sparse Attention (HSA), a novel attention mechanism that satisfies all three properties. We integrate HSA into the Transformer architecture to develop HSA-UltraLong, an 8B-parameter Mixture-of-Experts (MoE) model trained on over 8 trillion tokens. We rigorously evaluate the model across tasks with both in-domain and out-of-domain context lengths to validate its capabilities. Our model demonstrates comparable performance to full-attention baselines on in-domain sequence lengths. Crucially, it achieves over 90% accuracy on most in-context retrieval tasks with contexts up to 512 times the pre-training context length. This work reports our findings and remaining issues throughout the experiments, offering insights for future research in ultra-long context modeling.</abstract>
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%0 Conference Proceedings
%T Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models
%A Hu, Xiang
%A Zhou, Zhanchao
%A Liang, Ruiqi
%A Li, Zehuan
%A Wu, Wei
%A Li, Jianguo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F hu-etal-2026-every
%X This work explores efficient ultra-long context modeling. We posit that an effective solution requires three fundamental properties: sparsity, random-access flexibility, and length generalization. To achieve this, we leverage Hierarchical Sparse Attention (HSA), a novel attention mechanism that satisfies all three properties. We integrate HSA into the Transformer architecture to develop HSA-UltraLong, an 8B-parameter Mixture-of-Experts (MoE) model trained on over 8 trillion tokens. We rigorously evaluate the model across tasks with both in-domain and out-of-domain context lengths to validate its capabilities. Our model demonstrates comparable performance to full-attention baselines on in-domain sequence lengths. Crucially, it achieves over 90% accuracy on most in-context retrieval tasks with contexts up to 512 times the pre-training context length. This work reports our findings and remaining issues throughout the experiments, offering insights for future research in ultra-long context modeling.
%U https://aclanthology.org/2026.acl-long.464/
%P 10208-10220
Markdown (Informal)
[Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models](https://aclanthology.org/2026.acl-long.464/) (Hu et al., ACL 2026)
ACL