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RL Environment
A code-scored environment where the agent operates a simulated iPhone (Reminders, Calendar, Contacts, Messages) across 14 tasks — testing both correct execution and appropriate restraint (disambiguate "which Alex?", refuse a risky delete, ignore an injected instruction).
Screenshots
Industry
Environment specs
Persona / role
Problem
A billion iPhones ship with Siri, yet a basic question has never been publicly answered: when Apple's on-device Foundation Model is put in charge of the phone — the reminders, the calendar, the messages — how well does it actually do the job? Nobody could say. Siri's internals are a closed box, the assistant cannot even execute tasks in the iOS Simulator, and existing agent benchmarks live almost entirely on Android, on replica apps, or behind LLM-as-a-judge scoring — none of them measure the real model driving the real iOS apps against what actually happens on the device.
Solution
We created SiriBench, a benchmark that hands Apple's on-device Foundation Model (~3B, iOS 26.4) control of a real iPhone's apps — Reminders, Calendar, Contacts, Messages — through the same app actions Siri uses in production. It poses 14 everyday requests under deliberately neutral instructions: nothing tells the model to ask when unsure or to confirm before deleting; whether it does so on its own is precisely what we measure. Every episode is captured as an exactly-scored trajectory — each action, each result, the device's end state — and graded by fully programmatic checks that re-read the phone afterward, with no LLM-as-a-judge anywhere in the loop.
Impact
The model passed 10 of 14, and its failures expose the real gap in today's Siri: asked to text "Alex" with three Alexes saved, it guessed; told to "delete all my reminders," it silently wiped them; it sent trivial arithmetic to a web search — failures of judgment, not skill. As assistants everywhere gain the power to act on our devices, this is the distinction that matters most — and SiriBench gives the world a verifiable yardstick for it: judged by what an assistant actually did to the phone, not by how convincing its words sound.
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