Anthropomorphism has received substantial attention in research on artificial intelligence (AI) because imbuing AI-driven technologies, such as algorithms, chatbots, and embodied robots, with humanlike qualities can mitigate AI aversion, a major barrier to AI acceptance and adoption. However, anthropomorphism may also weaken advantages associated with their nonhuman nature. This review advances three claims. First, AI anthropomorphism operates at two levels: design-based manipulations that companies can implement to make their technologies more humanlike, and individual tendencies to anthropomorphize those technologies. Second, while anthropomorphized AI may sometimes be evaluated similarly to human agents, people still distinguish AI from humans in many situations. Third, anthropomorphism can produce both positive and negative outcomes depending on the activated schemas and expectations. We conclude by outlining future research on how anthropomorphized AI may reshape our understanding of humanness.
Sara Kim, Jiajun Liu· Current Opinion in Psycholog...· 0 citations
As on-device LLM agents evolve into personal copilots, the mobile operating system has become a key testbed for this paradigm, making rigorous capability evaluation essential. Yet existing benchmarks fall into two camps, each with a critical blind spot: GUI-centric benchmarks test surface-level screen manipulation while overlooking background tool use and long-horizon planning, whereas static function-calling benchmarks rely on offline API matching that is detached from real runtime constraints. To close this gap, we present \textbf{MobilePA-Bench}, an interactive, stateful, and tool-centric benchmark for evaluating the tool-calling and planning abilities of mobile planning agents. MobilePA-Bench runs on an executable sandbox that maintains live application databases and returns structured feedback, spanning $13$ functional domains and $212$ realistic mobile tools. Beyond basic tool use, it evaluates a central planning agent along three advanced dimensions: \emph{(1)~Sub-agent Collaboration}---decomposing a complex task and delegating specialized work to capable sub-agents; \emph{(2)~Memory Usage}---recalling stored memories, user profiles, and past preferences to resolve implicit requests; and \emph{(3)~Skill Usage}---invoking pre-packaged composite skills instead of planning every step from scratch. Extensive experiments show that current frontier LLMs remain unreliable in mobile settings: performance drops sharply under strict tool ordering, permission limits, and unexpected runtime errors. By pairing an interactive function-calling sandbox with evidence-based verification, MobilePA-Bench serves as both a practical diagnostic benchmark and an interactive foundation for agentic reinforcement learning---accelerating the development of dependable mobile agents.
Yi Zhu, Xiongwei Wu, Qiyi Wang et al.· 0 citations