@inproceedings{liu-etal-2026-focalorder,
title = "{F}ocal{O}rder: Focal Preference Optimization for Reading Order Detection",
author = "Liu, Fuyuan and
Yu, Dianyu and
Ren, He and
Liu, Nayu and
Kang, Xiaomian and
Qiu, Delai and
Zhang, Fa and
Zhen, Genpeng and
Liu, Shengping and
Jiaen, Liang and
Weihuang and
Wang, Yining and
Zhu, Junnan",
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.868/",
pages = "19012--19029",
ISBN = "979-8-89176-390-6",
abstract = "Reading order detection is the foundation of document understanding.Most existing methods rely on uniform supervision, implicitly assuming a constant difficulty distribution across layout regions. In this work, we challenge this assumption by revealing a critical flaw: Positional Disparity, a phenomenon where models demonstrate mastery over the deterministic start and end regions but suffer a performance collapse in the complex intermediate sections.This degradation arises because standard training allows the massive volume of easy patterns to drown out the learning signals from difficult layouts.To address this, we propose FocalOrder, a framework driven by Focal Preference Optimization (FPO).Specifically, FocalOrder employs adaptive difficulty discovery with exponential moving average mechanism to dynamically pinpoint hard-to-learn transitions, while introducing a difficulty-calibrated pairwise ranking objective to enforce global logical consistency.Extensive experiments demonstrate that FocalOrder establishes new state-of-the-art results on OmniDocBench v1.0 and Comp-HRDoc.Our compact model not only outperforms competitive specialized baselines but also significantly surpasses large-scale general VLMs.These results demonstrate that aligning the optimization with intrinsic structural ambiguity of documents is critical for mastering complex document structures."
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<abstract>Reading order detection is the foundation of document understanding.Most existing methods rely on uniform supervision, implicitly assuming a constant difficulty distribution across layout regions. In this work, we challenge this assumption by revealing a critical flaw: Positional Disparity, a phenomenon where models demonstrate mastery over the deterministic start and end regions but suffer a performance collapse in the complex intermediate sections.This degradation arises because standard training allows the massive volume of easy patterns to drown out the learning signals from difficult layouts.To address this, we propose FocalOrder, a framework driven by Focal Preference Optimization (FPO).Specifically, FocalOrder employs adaptive difficulty discovery with exponential moving average mechanism to dynamically pinpoint hard-to-learn transitions, while introducing a difficulty-calibrated pairwise ranking objective to enforce global logical consistency.Extensive experiments demonstrate that FocalOrder establishes new state-of-the-art results on OmniDocBench v1.0 and Comp-HRDoc.Our compact model not only outperforms competitive specialized baselines but also significantly surpasses large-scale general VLMs.These results demonstrate that aligning the optimization with intrinsic structural ambiguity of documents is critical for mastering complex document structures.</abstract>
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%0 Conference Proceedings
%T FocalOrder: Focal Preference Optimization for Reading Order Detection
%A Liu, Fuyuan
%A Yu, Dianyu
%A Ren, He
%A Liu, Nayu
%A Kang, Xiaomian
%A Qiu, Delai
%A Zhang, Fa
%A Zhen, Genpeng
%A Liu, Shengping
%A Jiaen, Liang
%A Wang, Yining
%A Zhu, Junnan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Weihuang
%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 liu-etal-2026-focalorder
%X Reading order detection is the foundation of document understanding.Most existing methods rely on uniform supervision, implicitly assuming a constant difficulty distribution across layout regions. In this work, we challenge this assumption by revealing a critical flaw: Positional Disparity, a phenomenon where models demonstrate mastery over the deterministic start and end regions but suffer a performance collapse in the complex intermediate sections.This degradation arises because standard training allows the massive volume of easy patterns to drown out the learning signals from difficult layouts.To address this, we propose FocalOrder, a framework driven by Focal Preference Optimization (FPO).Specifically, FocalOrder employs adaptive difficulty discovery with exponential moving average mechanism to dynamically pinpoint hard-to-learn transitions, while introducing a difficulty-calibrated pairwise ranking objective to enforce global logical consistency.Extensive experiments demonstrate that FocalOrder establishes new state-of-the-art results on OmniDocBench v1.0 and Comp-HRDoc.Our compact model not only outperforms competitive specialized baselines but also significantly surpasses large-scale general VLMs.These results demonstrate that aligning the optimization with intrinsic structural ambiguity of documents is critical for mastering complex document structures.
%U https://aclanthology.org/2026.acl-long.868/
%P 19012-19029
Markdown (Informal)
[FocalOrder: Focal Preference Optimization for Reading Order Detection](https://aclanthology.org/2026.acl-long.868/) (Liu et al., ACL 2026)
ACL
- Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, Weihuang, Yining Wang, and Junnan Zhu. 2026. FocalOrder: Focal Preference Optimization for Reading Order Detection. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19012–19029, San Diego, California, United States. Association for Computational Linguistics.