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    <abstract>Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual tokens. As interaction trajectories grow, the token cost increases rapidly, limiting the amount of history that can be incorporated under fixed context and compute budgets. This has resulted in no or very limited improvement in the performance when using history unlike other domains. We address this inefficiency by introducing ReVision, which is used to train multimodal language models on trajectories where redundant visual patches are removed using a learned patch selector that compares patch representations across consecutive screenshots while preserving spatial structure required by the model. Across three benchmarks, OSWorld, WebTailBench, and AgentNetBench, when processing trajectories with 5 history screenshots using Qwen2.5-VL-7B, ReVision reduces token usage by 46% on average while improving success rate by 3% over the no drop baseline. This establishes a clear efficiency gain, enabling agents to process longer trajectories with fewer tokens. With this improved efficiency, we revisit the role of history in CUAs and find that performance continues to improve as more past observations are incorporated when redundancy is removed.</abstract>
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%0 Conference Proceedings
%T ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction
%A Abaskohi, Amirhossein
%A He, Yuhang
%A West, Peter
%A Carenini, Giuseppe
%A Chawla, Pranit
%A Vineet, Vibhav
%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 abaskohi-etal-2026-revision
%X Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual tokens. As interaction trajectories grow, the token cost increases rapidly, limiting the amount of history that can be incorporated under fixed context and compute budgets. This has resulted in no or very limited improvement in the performance when using history unlike other domains. We address this inefficiency by introducing ReVision, which is used to train multimodal language models on trajectories where redundant visual patches are removed using a learned patch selector that compares patch representations across consecutive screenshots while preserving spatial structure required by the model. Across three benchmarks, OSWorld, WebTailBench, and AgentNetBench, when processing trajectories with 5 history screenshots using Qwen2.5-VL-7B, ReVision reduces token usage by 46% on average while improving success rate by 3% over the no drop baseline. This establishes a clear efficiency gain, enabling agents to process longer trajectories with fewer tokens. With this improved efficiency, we revisit the role of history in CUAs and find that performance continues to improve as more past observations are incorporated when redundancy is removed.
%U https://aclanthology.org/2026.findings-emnlp.2180/
%P 46621-46646

Markdown (Informal)

[ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction](https://aclanthology.org/2026.findings-emnlp.2180/) (Abaskohi et al., Findings 2026)

• ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction (Abaskohi et al., Findings 2026)

ACL

• Amirhossein Abaskohi, Yuhang He, Peter West, Giuseppe Carenini, Pranit Chawla, and Vibhav Vineet. 2026. ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 46621–46646, Budapest, Hungary. Association for Computational Linguistics.