@inproceedings{sun-etal-2026-sepseq,
title = "{S}ep{S}eq: A Training-Free Framework for Long Numerical Sequence Processing in {LLM}s",
author = "Sun, Jie and
Liu, Yu and
Han, Lu and
Deng, Qiwen and
Shu, Xiang and
Xiao, Yang and
Ma, Lintao and
Lu, Xingyu and
Zhou, Jun and
Liu, Pengfei and
Wu, Jiancan and
Wang, Xiang",
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.272/",
pages = "5522--5537",
ISBN = "979-8-89176-395-1",
abstract = "While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when processing long numerical sequences. We attribute this failure to the attention dispersion in the Softmax mechanism, which prevents the model from concentrating attention. To overcome this, we propose \textbf{Sep}arate \textbf{Seq}uence (\textbf{SepSeq}), a training-free, plug-and-play framework to mitigate dispersion by strategically inserting separator tokens. Mechanistically, we demonstrate that separator tokens act as an attention anchor, recalibrating attention to focus on local segments while preserving global context. Extensive evaluations on 9 widely-adopted LLMs confirm the effectiveness of our approach: SepSeq yields an average relative accuracy improvement of 35.6{\%} across diverse domains while reducing 16.4{\%} inference token consumption."
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<abstract>While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when processing long numerical sequences. We attribute this failure to the attention dispersion in the Softmax mechanism, which prevents the model from concentrating attention. To overcome this, we propose Separate Sequence (SepSeq), a training-free, plug-and-play framework to mitigate dispersion by strategically inserting separator tokens. Mechanistically, we demonstrate that separator tokens act as an attention anchor, recalibrating attention to focus on local segments while preserving global context. Extensive evaluations on 9 widely-adopted LLMs confirm the effectiveness of our approach: SepSeq yields an average relative accuracy improvement of 35.6% across diverse domains while reducing 16.4% inference token consumption.</abstract>
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%0 Conference Proceedings
%T SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs
%A Sun, Jie
%A Liu, Yu
%A Han, Lu
%A Deng, Qiwen
%A Shu, Xiang
%A Xiao, Yang
%A Ma, Lintao
%A Lu, Xingyu
%A Zhou, Jun
%A Liu, Pengfei
%A Wu, Jiancan
%A Wang, Xiang
%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 sun-etal-2026-sepseq
%X While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when processing long numerical sequences. We attribute this failure to the attention dispersion in the Softmax mechanism, which prevents the model from concentrating attention. To overcome this, we propose Separate Sequence (SepSeq), a training-free, plug-and-play framework to mitigate dispersion by strategically inserting separator tokens. Mechanistically, we demonstrate that separator tokens act as an attention anchor, recalibrating attention to focus on local segments while preserving global context. Extensive evaluations on 9 widely-adopted LLMs confirm the effectiveness of our approach: SepSeq yields an average relative accuracy improvement of 35.6% across diverse domains while reducing 16.4% inference token consumption.
%U https://aclanthology.org/2026.findings-acl.272/
%P 5522-5537
Markdown (Informal)
[SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs](https://aclanthology.org/2026.findings-acl.272/) (Sun et al., Findings 2026)
ACL
- Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, and Xiang Wang. 2026. SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5522–5537, San Diego, California, United States. Association for Computational Linguistics.