@inproceedings{lee-etal-2026-history,
title = "A History-Aware Visually Grounded Critic for Computer Use Agents",
author = "Lee, Jaewoo and
Khan, Zaid and
Prasad, Archiki and
Chen, Justin and
Chakraborty, Supriyo and
Balasubramaniam, Kartik and
Sahu, Sambit and
Stengel-Eskin, Elias and
Lee, Hyunji and
Bansal, Mohit",
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.2147/",
pages = "45877--45908",
ISBN = "979-8-89176-462-0",
abstract = "Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in complex Graphical User Interface (GUI) environments. However, existing critics suffer from two key limitations: they (1) focus primarily on short-sighted decision loops (e.g., forgetting earlier actions) and (2) lack the visual grounding needed to detect flawed actions (e.g., clicking wrong UI elements). To address these, we introduce HIVIG, a History-aware Visually Grounded test-time framework, built around a multimodal critic trained on real GUI trajectories to abstract past interactions into a compact record and to evaluate actions with visual grounding. At test time, HIVIG integrates the critic into the policy decision loop to provide macro-action history, which summarizes the policy{'}s completed achievements, and visually grounded critique, which verifies raw execution coordinates against the current screenshot to intercept errors before execution. Across web, mobile, and desktop benchmarks, HIVIG consistently outperforms existing scalar and verbal critics, improving average success rates over the strongest baseline by 5.8{\%} for Qwen3-VL-32B and 9.0{\%} for Gemini-3-Flash, and demonstrates strong cross-platform generalization. Ablations show that macro-action history mitigates short-sighted planning and visually grounded critique reduces execution errors, with both components being critical for test-time scaling in long-horizon GUI tasks."
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<abstract>Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in complex Graphical User Interface (GUI) environments. However, existing critics suffer from two key limitations: they (1) focus primarily on short-sighted decision loops (e.g., forgetting earlier actions) and (2) lack the visual grounding needed to detect flawed actions (e.g., clicking wrong UI elements). To address these, we introduce HIVIG, a History-aware Visually Grounded test-time framework, built around a multimodal critic trained on real GUI trajectories to abstract past interactions into a compact record and to evaluate actions with visual grounding. At test time, HIVIG integrates the critic into the policy decision loop to provide macro-action history, which summarizes the policy’s completed achievements, and visually grounded critique, which verifies raw execution coordinates against the current screenshot to intercept errors before execution. Across web, mobile, and desktop benchmarks, HIVIG consistently outperforms existing scalar and verbal critics, improving average success rates over the strongest baseline by 5.8% for Qwen3-VL-32B and 9.0% for Gemini-3-Flash, and demonstrates strong cross-platform generalization. Ablations show that macro-action history mitigates short-sighted planning and visually grounded critique reduces execution errors, with both components being critical for test-time scaling in long-horizon GUI tasks.</abstract>
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%0 Conference Proceedings
%T A History-Aware Visually Grounded Critic for Computer Use Agents
%A Lee, Jaewoo
%A Khan, Zaid
%A Prasad, Archiki
%A Chen, Justin
%A Chakraborty, Supriyo
%A Balasubramaniam, Kartik
%A Sahu, Sambit
%A Stengel-Eskin, Elias
%A Lee, Hyunji
%A Bansal, Mohit
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%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 lee-etal-2026-history
%X Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in complex Graphical User Interface (GUI) environments. However, existing critics suffer from two key limitations: they (1) focus primarily on short-sighted decision loops (e.g., forgetting earlier actions) and (2) lack the visual grounding needed to detect flawed actions (e.g., clicking wrong UI elements). To address these, we introduce HIVIG, a History-aware Visually Grounded test-time framework, built around a multimodal critic trained on real GUI trajectories to abstract past interactions into a compact record and to evaluate actions with visual grounding. At test time, HIVIG integrates the critic into the policy decision loop to provide macro-action history, which summarizes the policy’s completed achievements, and visually grounded critique, which verifies raw execution coordinates against the current screenshot to intercept errors before execution. Across web, mobile, and desktop benchmarks, HIVIG consistently outperforms existing scalar and verbal critics, improving average success rates over the strongest baseline by 5.8% for Qwen3-VL-32B and 9.0% for Gemini-3-Flash, and demonstrates strong cross-platform generalization. Ablations show that macro-action history mitigates short-sighted planning and visually grounded critique reduces execution errors, with both components being critical for test-time scaling in long-horizon GUI tasks.
%U https://aclanthology.org/2026.findings-emnlp.2147/
%P 45877-45908
Markdown (Informal)
[A History-Aware Visually Grounded Critic for Computer Use Agents](https://aclanthology.org/2026.findings-emnlp.2147/) (Lee et al., Findings 2026)
ACL
- Jaewoo Lee, Zaid Khan, Archiki Prasad, Justin Chen, Supriyo Chakraborty, Kartik Balasubramaniam, Sambit Sahu, Elias Stengel-Eskin, Hyunji Lee, and Mohit Bansal. 2026. A History-Aware Visually Grounded Critic for Computer Use Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 45877–45908, Budapest, Hungary. Association for Computational Linguistics.