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Step: Success- rate-aware trajectory-efficient policy optimization

5 Pith papers cite this work. Polarity classification is still indexing.

5 Pith papers citing it

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representative citing papers

PhoneWorld: Scaling Phone-Use Agent Environments

cs.CL · 2026-05-28 · unverdicted · novelty 6.0

PhoneWorld is a pipeline that converts real mobile trajectories into scalable controllable environments, yielding large gains on four benchmarks when used to supplement training data.

Xiaomi-GUI-0 Technical Report

cs.AI · 2026-06-30 · unverdicted · novelty 4.0 · 2 refs

Xiaomi-GUI-0 reports 72.0% success on RealMobile and 78.9% on AndroidWorld via real-device closed-loop training with multi-source data and three-stage RL pipeline.

How Mobile World Model Guides GUI Agents?

cs.AI · 2026-05-11 · unverdicted · novelty 4.0 · 2 refs

World models trained on delta text, full text, diffusion images, and renderable code achieve SoTA on two benchmarks and improve downstream GUI agent performance on three mobile datasets with modality-specific strengths.

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Showing 2 of 2 citing papers after filters.

  • Xiaomi-GUI-0 Technical Report cs.AI · 2026-06-30 · unverdicted · none · ref 8 · 2 links

    Xiaomi-GUI-0 reports 72.0% success on RealMobile and 78.9% on AndroidWorld via real-device closed-loop training with multi-source data and three-stage RL pipeline.

  • How Mobile World Model Guides GUI Agents? cs.AI · 2026-05-11 · unverdicted · none · ref 47 · 2 links

    World models trained on delta text, full text, diffusion images, and renderable code achieve SoTA on two benchmarks and improve downstream GUI agent performance on three mobile datasets with modality-specific strengths.