@inproceedings{he-etal-2026-trace2ground,
title = "{T}race2{G}round: Scenario, Disambiguation, and Relation Traces for Web {GUI} Grounding",
author = "He, Ruozhen and
Qu, Zhongnan and
Zhang, Sheng and
Unal, Mesut Erhan and
Tu, Zhuowen and
Appalaraju, Srikar and
Yuan, Jianbo and
Ghadar, Shabnam and
Liao, Haofu",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Rozanov, Nikolai and
Hashemzadeh, Maryam and
Arora, Akhil",
booktitle = "Proceedings of the Second Workshop for {RE}search on Agent Language Models ({REALM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.realm-1.100/",
pages = "1444--1470",
ISBN = "979-8-89176-485-9",
abstract = "We present Trace2Ground, a large-scale web GUI grounding dataset and benchmark built by parsing HTML/DOM to obtain candidate interface elements and using large language models to simulate interaction traces and natural-language instructions. Existing datasets primarily target instructions solvable by matching the target element{'}s visible text, while real-world GUI tasks may require grounding under user scenarios, duplicate candidates, and relational descriptions. Trace2Ground targets these under-explored stress regimes through three curated splits: PragmaMind, where instructions describe user scenarios without naming UI elements; ContextTwin, where multiple elements share identical visible text and the correct target must be selected using interaction context; and RelateLoc, where the target is specified via spatial or structural relations to nearby anchors. We construct the dataset via an automated pipeline that extracts candidates from HTML/DOM structure, synthesizes traces and instructions with LLMs, and applies multi-view visual checks. Strong specialist GUI grounders underperform on these regimes despite reaching around 90{\%} on ScreenSpot, exposing a gap not addressed by existing GUI training data. Standard supervised fine-tuning on Trace2Ground improves the base model{'}s performance on our curated regimes by 42.3{\%} on average and yields zero-shot transfer to standard GUI benchmarks, including a 9.8{\%} average improvement on the non-web ScreenSpot-Pro benchmark across six desktop domains."
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<abstract>We present Trace2Ground, a large-scale web GUI grounding dataset and benchmark built by parsing HTML/DOM to obtain candidate interface elements and using large language models to simulate interaction traces and natural-language instructions. Existing datasets primarily target instructions solvable by matching the target element’s visible text, while real-world GUI tasks may require grounding under user scenarios, duplicate candidates, and relational descriptions. Trace2Ground targets these under-explored stress regimes through three curated splits: PragmaMind, where instructions describe user scenarios without naming UI elements; ContextTwin, where multiple elements share identical visible text and the correct target must be selected using interaction context; and RelateLoc, where the target is specified via spatial or structural relations to nearby anchors. We construct the dataset via an automated pipeline that extracts candidates from HTML/DOM structure, synthesizes traces and instructions with LLMs, and applies multi-view visual checks. Strong specialist GUI grounders underperform on these regimes despite reaching around 90% on ScreenSpot, exposing a gap not addressed by existing GUI training data. Standard supervised fine-tuning on Trace2Ground improves the base model’s performance on our curated regimes by 42.3% on average and yields zero-shot transfer to standard GUI benchmarks, including a 9.8% average improvement on the non-web ScreenSpot-Pro benchmark across six desktop domains.</abstract>
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%0 Conference Proceedings
%T Trace2Ground: Scenario, Disambiguation, and Relation Traces for Web GUI Grounding
%A He, Ruozhen
%A Qu, Zhongnan
%A Zhang, Sheng
%A Unal, Mesut Erhan
%A Tu, Zhuowen
%A Appalaraju, Srikar
%A Yuan, Jianbo
%A Ghadar, Shabnam
%A Liao, Haofu
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F he-etal-2026-trace2ground
%X We present Trace2Ground, a large-scale web GUI grounding dataset and benchmark built by parsing HTML/DOM to obtain candidate interface elements and using large language models to simulate interaction traces and natural-language instructions. Existing datasets primarily target instructions solvable by matching the target element’s visible text, while real-world GUI tasks may require grounding under user scenarios, duplicate candidates, and relational descriptions. Trace2Ground targets these under-explored stress regimes through three curated splits: PragmaMind, where instructions describe user scenarios without naming UI elements; ContextTwin, where multiple elements share identical visible text and the correct target must be selected using interaction context; and RelateLoc, where the target is specified via spatial or structural relations to nearby anchors. We construct the dataset via an automated pipeline that extracts candidates from HTML/DOM structure, synthesizes traces and instructions with LLMs, and applies multi-view visual checks. Strong specialist GUI grounders underperform on these regimes despite reaching around 90% on ScreenSpot, exposing a gap not addressed by existing GUI training data. Standard supervised fine-tuning on Trace2Ground improves the base model’s performance on our curated regimes by 42.3% on average and yields zero-shot transfer to standard GUI benchmarks, including a 9.8% average improvement on the non-web ScreenSpot-Pro benchmark across six desktop domains.
%U https://aclanthology.org/2026.realm-1.100/
%P 1444-1470
Markdown (Informal)
[Trace2Ground: Scenario, Disambiguation, and Relation Traces for Web GUI Grounding](https://aclanthology.org/2026.realm-1.100/) (He et al., REALM 2026)
ACL
- Ruozhen He, Zhongnan Qu, Sheng Zhang, Mesut Erhan Unal, Zhuowen Tu, Srikar Appalaraju, Jianbo Yuan, Shabnam Ghadar, and Haofu Liao. 2026. Trace2Ground: Scenario, Disambiguation, and Relation Traces for Web GUI Grounding. In Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026), pages 1444–1470, Budapest, Hungary. Association for Computational Linguistics.