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arXiv AI By Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Yusuke Kato, Kazuki Kozuka, Aditya Grover

MobileWorldBench: Towards Semantic World Modeling For Mobile Agents

Read the original on arXiv AI →

MobileWorldBench introduces a benchmark for evaluating vision‑language models as world models in mobile GUI agents, shifting from pixel‑based predictions to natural‑language state transitions. The authors release MobileWorld, a 1.4 million‑sample dataset that enhances VLM world‑modeling performance. They also present a framework that integrates these semantic world models into mobile agent planning, showing improved task success rates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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