arXiv Machine Learning
Jun 9
iOSWorld: A Benchmark for Personally Intelligent Phone Agents
arXiv:2606. 09764v1 Announce Type: new Abstract: A useful phone agent needs to be personally intelligent.
Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker
The paper examines the use of large language models (LLMs) to reverse‑engineer the protobuf schemas that iPhones send to Apple’s location services. Using Claude Opus 4.8 and Codex GPT‑Sol 5.6, the authors find that most AI‑generated schemas contain significant errors that are difficult to detect, making direct AI assistance unsuitable for forensic or privacy analysis. However, they demonstrate that an LLM can be employed to build a reliable Python tool, Extractor, which accurately recovers protobuf schemas from Apple binaries and successfully decodes network traffic from a jailbroken iPhone 8 running iOS 16.7.11.
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