GUI agents are deployed with frozen weights and discard everything they experience on the job. Existing ways to update an agent's weights assume something deployment withholds: ground truth, rollouts beyond the single attempt (retries, samples, practice runs), or a learning phase other than deployment. Because GUI acti...
Zi-Qiang Wang, Li Gu, Zhixiang Chi et al.· 0 citations
This work introduces WidgetGen, a lightweight tool-grounded framework that extracts observable text and color evidence, performs high-level layout and optional chart reasoning, and directly generates executable JavaScript XML (JSX) while avoiding a fixed UI schema.
Houston H. Zhang, Tao Zhang, Li Gu et al.· 0 citations
This work introduces MobileJudgeBench, a benchmark for systematically evaluating LLM-as-judge methods on mobile agent trajectories, and reveals benchmark quality metrics reliably predict real-world judge utility.
Zi-Qiang Wang, Li Gu, Zhixiang Chi et al.· 0 citations
A new FSTT-DA framework that integrates LoRA fine-tuning with model merging and proposes a hypernetwork trained via meta-learning that generates per-column merging factors to combine LoRA modules to adapt the learned knowledge to a specific target domain.
Siobhan Reid, Zhixiang Chi, Li Gu et al.· arXiv.org· 0 citations
StepReflect is proposed, which formulates per-step GUI reflection as supervised structured prediction conditioned on explicit transition specifications and paired visual evidence, and established as a practical, locally deployable alternative to repeated frontier-model reflection for long-horizon mobile GUI agents.
Lin-Qiang Guo, Wei Liu, Li Gu et al.· 0 citations
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