SPIEval is introduced, a human-curated benchmark grounded in five cognitive capabilities that exposes fundamental limitations of current LLM-based mobile assistants and motivates future research in this direction.
Junjie Ye, Zhuohui Sheng, Shao-Hua Liu et al.· 0 citations
This work introduces EvolveScaler, a code-driven framework that defines information evolution before rendering it as natural language, and shows that code-driven IE synthesis provides both challenging evaluation and transferable training supervision.
Ziliang Zhao, Ze-Nan Xu, Shuting Wang et al.· 0 citations
Pera describes a persistent agent organized around perception and control components that continually perceive service-relevant signals from episodic task executions, internal context, and changes in the surrounding environment, and use these signals to construct lifecycle tasks.
Shi-Han Dou, Haoxiang Jia, Shichun Liu et al.· 1 citation
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