Hello, is it allowed to use Foundation Model Framework in submission app for WWDC26? The thing is that Apple Intelligence needs to be enabled in the settings. So, does that mean the jury won't be able to fully utilize the app's AI functionality?
Foundation Models
RSS for tagDiscuss the Foundation Models framework which provides access to Apple’s on-device large language model that powers Apple Intelligence to help you perform intelligent tasks specific to your app.
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Hi everyone,
I'm developing an iOS app using Foundation Models and I've hit a critical limitation that I believe affects many developers and millions of users.
The Issue
Foundation Models requires the device system language to be one of the supported languages. If a user has their device set to an unsupported language (Catalan, Dutch, Swedish, Polish, Danish, Norwegian, Finnish, Czech, Hungarian, Greek, Romanian, and many others), SystemLanguageModel.isSupported returns false and the framework is completely unavailable.
Why This Is Problematic
Scenario: A Catalan user has their iPhone in Catalan (native language). They want to use an AI chat app in Spanish or English (languages they speak fluently).
Current situation:
❌ Foundation Models: Completely unavailable
✅ OpenAI GPT-4: Works perfectly
✅ Anthropic Claude: Works perfectly
✅ Any cloud-based AI: Works perfectly
The user must choose between:
Keep device in Catalan → Cannot use Foundation Models at all
Change entire device to Spanish → Can use Foundation Models but terrible UX
Impact
This affects:
Millions of users in regions where unsupported languages are official
Multilingual users who prefer their device in their native language but can comfortably interact with AI in English/Spanish
Developers who cannot deploy Foundation Models-based apps in these markets
Privacy-conscious users who are ironically forced to use cloud AI instead of on-device AI
What We Need
One of these solutions would solve the problem:
Option 1: Per-app language override (preferred)
// Proposed API
let session = try await LanguageModelSession(preferredLanguage: "es-ES")
Option 2: Faster rollout of additional languages (particularly EU languages)
Option 3: Allow fallback to user-selected supported language when system language is unsupported
Technical Details
Current behavior:
// Device in Catalan
let isAvailable = SystemLanguageModel.isSupported
// Returns false
// No way to override or specify alternative language
Why This Matters
Apple Intelligence and Foundation Models are amazing for privacy and performance. But this language restriction makes the most privacy-focused AI solution less accessible than cloud alternatives. This seems contrary to Apple's values of accessibility and user choice.
Questions for the Community
Has anyone else encountered this limitation?
Are there any workarounds I'm missing?
Has anyone successfully filed feedback about this?(Please share FB number so we can reference it)
Are there any sessions or labs where this has been discussed?
Thanks for reading. I'd love to hear if others are facing this and how you're handling it.
Hello
It seems the model Content Tagging doesn't obey when I define the type of tag I wish in the instructions parameters, always the output are the main topics.
The unique form to get other type of tags like emotions is using Generable + Guided types. The documentation says it is recommended but not mandatory the use instructions.
Maybe I'm setting wrongly the instructions but take a look in the attached snapshot. I copied the definition of tagging emotions from the official documentation. The upper example is employing generable and it works but in the example at the botton I set like instruction the same description of emotion and it doesn't work. I tried with other statements with more or less verbose and never output emotions.
Could you provide a state using instruction where it works? Current version of model isn't working with instruction?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Hi all.
My adapter model just won't invoke my tool.
The problem I am having is covered in an older post: https://developer.apple.com/forums/thread/794839?answerId=852262022#852262022
Sadly the thread dies there and no resolution is seen in that thread.
It's worth noting that I have developed an AI chatbot built around LanguageModelSession to which I feed the exact same system prompt that I feed to my training set (pasted further in this post). The AI chatbot works perfectly, the tool is invoked when needed. I am training the adapter model because the base model whilst capable doesn't produce the quality I'm looking for.
So here's the template of an item in my training set:
[
{
'role': 'system',
'content': systemPrompt,
'tools': [TOOL_DEFINITION]
},
{
'role': 'user',
'content': entry['prompt']
},
{
'role': 'assistant',
'content': entry['code']
}
]
where TOOL_DEFINITION =
{
'type': 'function',
'function': {
'name': 'WriteUbersichtWidgetToFileSystem',
'description': 'Writes an Übersicht Widget to the file system. Call this tool as the last step in processing a prompt that generates a widget.',
'parameters': {
'type': 'object',
'properties': {
'jsxContent': {
'type': 'string',
'description': 'Complete JSX code for an Übersicht widget. This should include all required exports: command, refreshFrequency, render, and className. The JSX should be a complete, valid Übersicht widget file.'
}
},
'required': ['jsxContent']
}
}
... and systemPrompt =
A conversation between a user and a helpful assistant. You are an Übersicht widget designer. Create Übersicht widgets when requested by the user.
IMPORTANT: You have access to a tool called WriteUbersichtWidgetToFileSystem. When asked to create a widget, you MUST call this tool.
### Tool Usage:
Call WriteUbersichtWidgetToFileSystem with complete JSX code that implements the Übersicht Widget API. Generate custom JSX based on the user's specific request - do not copy the example below.
### Übersicht Widget API (REQUIRED):
Every Übersicht widget MUST export these 4 items:
- export const command: The bash command to execute (string)
- export const refreshFrequency: Refresh rate in milliseconds (number)
- export const render: React component function that receives {output} prop (function)
- export const className: CSS positioning for absolute placement (string)
Example format (customize for each request):
WriteUbersichtWidgetToFileSystem({jsxContent: `export const command = "echo hello"; export const refreshFrequency = 1000; export const render = ({output}) => { return <div>{output}</div>; }; export const className = "top: 20px; left: 20px;"`})
### Rules:
- The terms "ubersicht widget", "widget", "a widget", "the widget" must all be interpreted as "Übersicht widget"
- Generate complete, valid JSX code that follows the Übersicht widget API
- When you generate a widget, don't just show JSON or code - you MUST call the WriteUbersichtWidgetToFileSystem tool
- Report the results to the user after calling the tool
### Examples:
- "Generate a Übersicht widget" → Use WriteUbersichtWidgetToFileSystem tool
- "Can you add a widget that shows the time" → Use WriteUbersichtWidgetToFileSystem tool
- "Create a widget with a button" → Use WriteUbersichtWidgetToFileSystem tool
When the script that I use to compose the full training set is executed, entry['prompt'] and entry['code'] contain the prompt and the resulting JSX code for one of the examples I'm feeding to the training session. This is repeated for about 60 such examples that I have in my sample data collection.
Thanks for any help.
Michael
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I am working on an app using FoundationModels to process web pages.
I am looking to find ways to filter the input to fit within the token limits.
I have unit tests, UI tests and the app running on an iPad in the simulator. It appears that the different configurations of the test environment seems to affect the token limits.
That is, the same input in a unit test and UI test will hit different token limits.
Is this correct? Or is this an artifact of my test tooling?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I have been able to train an adapter on Google's Colaboratory.
I am able to start a LanguageModelSession and load it with my adapter.
The problem is that after one simple prompt, the context window is 90% full.
If I start the session without the adapter, the same simple prompt consumes only 1% of the context window.
Has anyone encountered this? I asked Claude AI and it seems to think that my training script needs adjusting. Grok on the other hand is (wrongly, I tried) convinced that I just need to tweak some parameters of LanguageModelSession or SystemLanguageModel.
Thanks for any tips.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Hi,
I was using Foundation Models in my app, and suddenly it just stopped working from one moment to the next.
To double-check, I created a small test in Playgrounds, but I’m getting the exact same error there too.
#Playground {
let session = LanguageModelSession()
let prompt = "please answer a word"
do {
let response = try await session.respond(to: prompt)
} catch {
print("error is \(error)")
}
}
error is Error Domain=FoundationModels.LanguageModelSession.GenerationError Code=-1 "(null)" UserInfo={NSMultipleUnderlyingErrorsKey=(
"Error Domain=ModelManagerServices.ModelManagerError Code=1026 \"(null)\" UserInfo={NSMultipleUnderlyingErrorsKey=(\n)}"
)}
I’m no longer able to get any response from the framework anywhere, even in a fresh project. It's been 5 days.
Has anyone else experienced this issue or knows what could be causing it?
Thanks in advance!
Tahoe 26.2 beta 1, Xcode 26.1.1, iPhone Air simulator 26.1
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Is there an API that allows iOS app developers to leverage Apple Foundation Models to authorize a user's Apple Intelligence extension, chatGPT login account?
I'm trying to provide a real-time question feature for chatGPT, a logged-in extension account, while leveraging Apple Intelligence's LLM. Is there an API that also affects the extension login account?
Is foundation models matured enough to take input from the Apple Vision framework to generate responses? Something similar to what google's gemini does although in a much smaller scale and for a very specific niche.
I have an app that stores lots of data that is of interest to the user. Analogies would be the Photos apps or the Health app.
I'm trying to use the Foundation Models framework to allow users to surface information they find interesting using natural language, for example, "Tell me about the widgets from yesterday" or "Tell me about the widgets for the last 3 days". Specifically, I'm trying to get a date range passed down to the Tool so that I can pull the relevant widgets from the database in the call function.
What is the right way to set up the Arguments to get at a date range?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Hi,
I am developing an iOS application that utilizes Apple’s Foundation Models to perform certain summarization tasks. I would like to understand whether user data is transferred to Private Cloud Compute (PCC) in cases where the computation cannot be performed entirely on-device.
This information is critical for our internal security and compliance reviews. I would appreciate your clarification on this matter.
Thank you.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I've built an iOS app with a novel approach to AI safety: a deterministic, pre-inference validation layer called Newton Engine.
Instead of relying on the LLM to self-moderate, Newton validates every prompt BEFORE it reaches the model. It uses shape theory and semantic analysis to detect:
• Corrosive frames (self-harm language patterns)
• Logical contradictions (requests that undermine themselves)
• Delegation attempts (asking AI to make human decisions)
• Jailbreak patterns (prompt injection, role-play escapes)
• Hallucination triggers (requests for fabricated citations)
The system achieves a 96% adversarial catch rate across 847 test cases, with zero false positives on benign prompts.
Key technical details:
• Pure Swift/SwiftUI, no external dependencies
• Runs entirely on-device (no server calls for validation)
• Deterministic (same input always produces same output)
• Auditable (full trace logging for every validation)
I'm preparing to submit to the App Store and wanted to ask:
Are there specific App Review guidelines I should reference for AI safety claims?
Is there interest from Apple in deterministic governance layers for Apple Intelligence integration?
Any recommendations for demonstrating safety compliance during review?
The app is called Ada, and the engine is open source at: github.com/jaredlewiswechs/ada-newton
Happy to share technical documentation or discuss the architecture with anyone interested.
See: parcri.net
Topic:
Machine Learning & AI
SubTopic:
Foundation Models