@inproceedings{wu-etal-2026-ui,
title = "{UI}-Oceanus: Scaling {GUI} Agents with Synthetic Environmental Dynamics",
author = "Wu, Mengzhou and
Guo, Yuzhe and
Cao, Yuan and
Lu, Haochuan and
Dfshenzhu and
Qu, Pingzhe and
Chen, Xin and
Kangqin and
Wang, Zhongpu and
Zhang, Xiaode and
Wang, Xinyi and
Dai, Wei and
Cao, Gang and
Deng, Yuetang and
Zhi, Gong and
Ran, Dezhi and
Li, Linyi and
Yang, Wei and
Xie, Tao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2168/",
pages = "46381--46399",
ISBN = "979-8-89176-462-0",
abstract = "Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and reliance on ungrounded synthetic teacher supervision. To transcend these limitations, we propose , a framework that shifts the learning focus from mimicking high-level trajectories to learning interaction dynamics from executed transitions. Through a systematic investigation of self-supervised objectives, we identify that forward dynamics, defined as the generative prediction of future interface states, acts as the primary driver for scalability and outperforms inverse and backward objectives. leverages this insight by converting low-cost autonomous exploration into high-density generative supervision to learn a semantic transition world model from executed transitions. Experimental evaluations across a series of models demonstrate consistent gains from our approach: models utilizing Continual Pre-Training (CPT) on synthetic dynamics outperform non-CPT baselines with an average success rate improvement of 7{\%} on offline benchmarks, which amplifies to a 16.8{\%} gain in real-world online navigation. Furthermore, we observe that navigation performance scales with synthetic data volume. These results suggest that grounding agents in forward predictive modeling offers a promising pathway to scalable GUI automation with cross-domain adaptability and compositional generalization."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wu-etal-2026-ui">
<titleInfo>
<title>UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mengzhou</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuzhe</namePart>
<namePart type="family">Guo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuan</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haochuan</namePart>
<namePart type="family">Lu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name>
<namePart>Dfshenzhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pingzhe</namePart>
<namePart type="family">Qu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xin</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name>
<namePart>Kangqin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhongpu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaode</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xinyi</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wei</namePart>
<namePart type="family">Dai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gang</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuetang</namePart>
<namePart type="family">Deng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gong</namePart>
<namePart type="family">Zhi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dezhi</namePart>
<namePart type="family">Ran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Linyi</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wei</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tao</namePart>
<namePart type="family">Xie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and reliance on ungrounded synthetic teacher supervision. To transcend these limitations, we propose , a framework that shifts the learning focus from mimicking high-level trajectories to learning interaction dynamics from executed transitions. Through a systematic investigation of self-supervised objectives, we identify that forward dynamics, defined as the generative prediction of future interface states, acts as the primary driver for scalability and outperforms inverse and backward objectives. leverages this insight by converting low-cost autonomous exploration into high-density generative supervision to learn a semantic transition world model from executed transitions. Experimental evaluations across a series of models demonstrate consistent gains from our approach: models utilizing Continual Pre-Training (CPT) on synthetic dynamics outperform non-CPT baselines with an average success rate improvement of 7% on offline benchmarks, which amplifies to a 16.8% gain in real-world online navigation. Furthermore, we observe that navigation performance scales with synthetic data volume. These results suggest that grounding agents in forward predictive modeling offers a promising pathway to scalable GUI automation with cross-domain adaptability and compositional generalization.</abstract>
<identifier type="citekey">wu-etal-2026-ui</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.2168/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>46381</start>
<end>46399</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics
%A Wu, Mengzhou
%A Guo, Yuzhe
%A Cao, Yuan
%A Lu, Haochuan
%A Qu, Pingzhe
%A Chen, Xin
%A Wang, Zhongpu
%A Zhang, Xiaode
%A Wang, Xinyi
%A Dai, Wei
%A Cao, Gang
%A Deng, Yuetang
%A Zhi, Gong
%A Ran, Dezhi
%A Li, Linyi
%A Yang, Wei
%A Xie, Tao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Dfshenzhu
%A Kangqin
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wu-etal-2026-ui
%X Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and reliance on ungrounded synthetic teacher supervision. To transcend these limitations, we propose , a framework that shifts the learning focus from mimicking high-level trajectories to learning interaction dynamics from executed transitions. Through a systematic investigation of self-supervised objectives, we identify that forward dynamics, defined as the generative prediction of future interface states, acts as the primary driver for scalability and outperforms inverse and backward objectives. leverages this insight by converting low-cost autonomous exploration into high-density generative supervision to learn a semantic transition world model from executed transitions. Experimental evaluations across a series of models demonstrate consistent gains from our approach: models utilizing Continual Pre-Training (CPT) on synthetic dynamics outperform non-CPT baselines with an average success rate improvement of 7% on offline benchmarks, which amplifies to a 16.8% gain in real-world online navigation. Furthermore, we observe that navigation performance scales with synthetic data volume. These results suggest that grounding agents in forward predictive modeling offers a promising pathway to scalable GUI automation with cross-domain adaptability and compositional generalization.
%U https://aclanthology.org/2026.findings-emnlp.2168/
%P 46381-46399
Markdown (Informal)
[UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics](https://aclanthology.org/2026.findings-emnlp.2168/) (Wu et al., Findings 2026)
ACL
- Mengzhou Wu, Yuzhe Guo, Yuan Cao, Haochuan Lu, Dfshenzhu, Pingzhe Qu, Xin Chen, Kangqin, Zhongpu Wang, Xiaode Zhang, Xinyi Wang, Wei Dai, Gang Cao, Yuetang Deng, Gong Zhi, Dezhi Ran, Linyi Li, Wei Yang, and Tao Xie. 2026. UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 46381–46399, Budapest, Hungary. Association for Computational Linguistics.