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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...
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.
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.
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