@inproceedings{zhang-etal-2026-cuadebug,
title = "{CUAD}ebug: Diagnosing and Repairing Computer-Use Agent Failures",
author = "Zhang, Weijia and
Zhu, Kunlun and
Liu, Zeyi and
Chen, Yinting and
Ma, Tianyi and
Liu, Jiateng and
Zhang, Jiaxun and
Li, Bingxuan and
Lu, Pan and
Tang, Xiangru and
Ji, Heng and
You, Jiaxuan",
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.530/",
pages = "11110--11135",
ISBN = "979-8-89176-462-0",
abstract = "Computer-use agents (CUAs) interact with graphical interfaces through screenshots and low-level mouse and keyboard actions, yet the causal error may precede the terminal failure. We present **CUADebug**, a framework for localizing root causes in CUA trajectories and guiding re-execution. **CUADebug** includes a five-category, 30-subtype taxonomy; CUAErrorBench, a benchmark of 204 failed OSWorld trajectories with human root-cause annotations; and CUADebugger, a ReAct-style agent for root-cause analysis (RCA). CUADebugger iteratively selects trajectory steps, inspects paired before/after screenshots and action traces, and submits a structured diagnosis containing the causal step, taxonomy label, grounded evidence, and correction. CUADebugger performs RCA without per-trajectory human intervention; human annotations are used to evaluate RCA predictions and, in controlled re-rollout comparisons, to fix restart points. Task reasoning and control is the largest annotated failure category (110/204). CUADebugger improves L2 and Tag+Step Exact across three debugger backbones on the Claude-agent split; with Gemini 2.5 Pro, Tag+Step Exact rises from 11.1{\%} to 19.4{\%}. Single re-execution improves failure recovery from 13.89{\%} to 29.86{\%} (overall 61.77{\%} to 68.14{\%}); controlled continual re-execution improves it from 12.50{\%} to 25.69{\%} (overall 61.22{\%} to 66.48{\%}). Project page: cuadebug.github.io"
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<abstract>Computer-use agents (CUAs) interact with graphical interfaces through screenshots and low-level mouse and keyboard actions, yet the causal error may precede the terminal failure. We present **CUADebug**, a framework for localizing root causes in CUA trajectories and guiding re-execution. **CUADebug** includes a five-category, 30-subtype taxonomy; CUAErrorBench, a benchmark of 204 failed OSWorld trajectories with human root-cause annotations; and CUADebugger, a ReAct-style agent for root-cause analysis (RCA). CUADebugger iteratively selects trajectory steps, inspects paired before/after screenshots and action traces, and submits a structured diagnosis containing the causal step, taxonomy label, grounded evidence, and correction. CUADebugger performs RCA without per-trajectory human intervention; human annotations are used to evaluate RCA predictions and, in controlled re-rollout comparisons, to fix restart points. Task reasoning and control is the largest annotated failure category (110/204). CUADebugger improves L2 and Tag+Step Exact across three debugger backbones on the Claude-agent split; with Gemini 2.5 Pro, Tag+Step Exact rises from 11.1% to 19.4%. Single re-execution improves failure recovery from 13.89% to 29.86% (overall 61.77% to 68.14%); controlled continual re-execution improves it from 12.50% to 25.69% (overall 61.22% to 66.48%). Project page: cuadebug.github.io</abstract>
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%0 Conference Proceedings
%T CUADebug: Diagnosing and Repairing Computer-Use Agent Failures
%A Zhang, Weijia
%A Zhu, Kunlun
%A Liu, Zeyi
%A Chen, Yinting
%A Ma, Tianyi
%A Liu, Jiateng
%A Zhang, Jiaxun
%A Li, Bingxuan
%A Lu, Pan
%A Tang, Xiangru
%A Ji, Heng
%A You, Jiaxuan
%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 zhang-etal-2026-cuadebug
%X Computer-use agents (CUAs) interact with graphical interfaces through screenshots and low-level mouse and keyboard actions, yet the causal error may precede the terminal failure. We present **CUADebug**, a framework for localizing root causes in CUA trajectories and guiding re-execution. **CUADebug** includes a five-category, 30-subtype taxonomy; CUAErrorBench, a benchmark of 204 failed OSWorld trajectories with human root-cause annotations; and CUADebugger, a ReAct-style agent for root-cause analysis (RCA). CUADebugger iteratively selects trajectory steps, inspects paired before/after screenshots and action traces, and submits a structured diagnosis containing the causal step, taxonomy label, grounded evidence, and correction. CUADebugger performs RCA without per-trajectory human intervention; human annotations are used to evaluate RCA predictions and, in controlled re-rollout comparisons, to fix restart points. Task reasoning and control is the largest annotated failure category (110/204). CUADebugger improves L2 and Tag+Step Exact across three debugger backbones on the Claude-agent split; with Gemini 2.5 Pro, Tag+Step Exact rises from 11.1% to 19.4%. Single re-execution improves failure recovery from 13.89% to 29.86% (overall 61.77% to 68.14%); controlled continual re-execution improves it from 12.50% to 25.69% (overall 61.22% to 66.48%). Project page: cuadebug.github.io
%U https://aclanthology.org/2026.findings-emnlp.530/
%P 11110-11135
Markdown (Informal)
[CUADebug: Diagnosing and Repairing Computer-Use Agent Failures](https://aclanthology.org/2026.findings-emnlp.530/) (Zhang et al., Findings 2026)
ACL
- Weijia Zhang, Kunlun Zhu, Zeyi Liu, Yinting Chen, Tianyi Ma, Jiateng Liu, Jiaxun Zhang, Bingxuan Li, Pan Lu, Xiangru Tang, Heng Ji, and Jiaxuan You. 2026. CUADebug: Diagnosing and Repairing Computer-Use Agent Failures. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 11110–11135, Budapest, Hungary. Association for Computational Linguistics.