Graphical user interface (GUI) agents built on vision-language models have emerged as a promising approach to automate human-computer workflows. However, they also face the inefficiency challenge as they process long sequences of high-resolution screenshots and solving long-horizon tasks, making inference slow, costly...
Kung-Hsiang Huang, Haoyi Qiu, Yu-Tong Dai et al.· Trans. Mach. Learn. Res.· 12 citations· ⚡3
Evaluating state-of-the-art video generators, image generators, and LLM/VLM baselines reveals a sharp gap between visual quality and reasoning correctness: video models perform best on Physical Commonsense, but remain weak on symbolic tasks such as Sudoku, ARC, and Math, and brittle in cross-view embodied navigation.
Ze-Fan Cai, Haoyi Qiu, Tian-Yi Ma et al.· 9 citations· ⚡1
This work introduces a controlled testbed for studying how goal-directed persuaders shift elicited stances in networks of LLM agents grounded in real-world ego-network topologies, and argues for evaluating multi-agent persuasion as a trajectory- and exposure-level process.
Haoyi Qiu, Genglin Liu, P. Venkit et al.· 0 citations
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