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Trace2World reconstructs app worlds for scalable GUI agent training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Trace2World, a novel system designed to reconstruct application worlds from real user interaction traces for more scalable GUI agent training. This method separates global behavior reconstruction from implementation, creating a shared app model that can compile into an executable environment. Trace2World demonstrated significant improvements in reproducing workflows and maintaining simulated training gains on real devices compared to existing baselines. The system also enhanced agent performance on various GUI benchmarks and real-device tasks. AI

IMPACT Enables more scalable and accurate training of GUI agents by improving environment reconstruction from real-world data.

RANK_REASON The cluster contains a research paper detailing a new method for GUI agent training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Trace2World reconstructs app worlds for scalable GUI agent training

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4 / 100
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The cluster contains a research paper detailing a new method for GUI agent training. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, infra
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zezhou Wang, Hanzhang Zhou, Panrong Tong, Quyu Kong, Pengxiang Li, Steven Hoi, Zhuzhong Qian, Yue Wang ·

    Trace2World: Reconstructing App Worlds from Real Interaction Traces for Scalable GUI Agent Training

    arXiv:2610.30792v1 Announce Type: new Abstract: Scaling GUI reinforcement learning requires environments that preserve real-app behavior. Yet coupling behavior inference with coding can turn partial traces into runnable mocks with incorrect state transitions. We introduce \textbf…

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