We’ve encountered what appears to be a CoreML regression between macOS 26.0.1 and macOS 26.1 Beta.
In macOS 26.0.1, CoreML models run and produce correct results. However, in macOS 26.1 Beta, the same models produce scrambled or corrupted outputs, suggesting that tensor memory is being read or written incorrectly. The behavior is consistent with a low-level stride or pointer arithmetic issue — for example, using 16-bit strides on 32-bit data or other mismatches in tensor layout handling.
Reproduction
Install ON1 Photo RAW 2026 or ON1 Resize 2026 on macOS 26.0.1.
Use the newest Highest Quality resize model, which is Stable Diffusion–based and runs through CoreML.
Observe correct, high-quality results.
Upgrade to macOS 26.1 Beta and run the same operation again.
The output becomes visually scrambled or corrupted.
We are also seeing similar issues with another Stable Diffusion UNet model that previously worked correctly on macOS 26.0.1. This suggests the regression may affect multiple diffusion-style architectures, likely due to a change in CoreML’s tensor stride, layout computation, or memory alignment between these versions.
Notes
The affected models are exported using standard CoreML conversion pipelines.
No custom operators or third-party CoreML runtime layers are used.
The issue reproduces consistently across multiple machines.
It would be helpful to know if there were changes to CoreML’s tensor layout, precision handling, or MLCompute backend between macOS 26.0.1 and 26.1 Beta, or if this is a known regression in the current beta.
Explore the power of machine learning and Apple Intelligence within apps. Discuss integrating features, share best practices, and explore the possibilities for your app here.
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Has anyone been able to run Tensorflow > 2.15 with Tensorflow Metal 1.1.0 on M3? I tried several times but was not successful. Seems like development on TensorFlow Metal has paused?
My iOS app supports iOS 18, and I’m using an encrypted CoreML model secured with a key generated from Xcode.
Every few months (around every 3 months), the encrypted model fails to load for both me and my users. When I investigate, I find this error:
coreml Fetching decryption key from server failed: noEntryFound("No records found"). Make sure the encryption key was generated with correct team ID
To temporarily fix it, I delete the old key, generate a new one, re-encrypt the model, and submit an app update. This resolves the issue, but only for a while.
This is a terrible experience for users and obviously not a sustainable solution.
I want to understand:
Why is this happening?
Is there a known expiration or invalidation policy for CoreML encryption keys?
How can I prevent this issue permanently?
Any insights or official guidance would be really appreciated.
Hi, The most recent version of tensorflow-metal is only available for macosx 12.0 and python up to version 3.11. Is there any chance it could be updated with wheels for macos 15 and Python 3.12 (which is the default version supported for tensrofllow 2.17+)? I'd note that even downgrading to Python 3.11 would not be sufficient, as the wheels only work for macos 12.
Thanks.
Hi all, I'm tuning my app prediction speed with Core ML model. I watched and tried the methods in video: Improve Core ML integration with async prediction and Optimize your Core ML usage. I also use instruments to look what's the bottleneck that my prediction speed cannot be faster.
Below is the instruments result with my app. its prediction duration is 10.29ms
And below is performance report shows the average speed of prediction is 5.55ms, that is about half time of my app prediction!
Below is part of my instruments records. I think the prediction should be considered quite frequent. Could it be faster?
How to be the same prediction speed as performance report? The prediction speed on macbook Pro M2 is nearly the same as macbook Air M1!
Here's the result:
Very weird.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I am using Foundation Models for the first time and no response is being provided to me.
Code
import Playgrounds
import FoundationModels
#Playground {
let session = LanguageModelSession()
let result = try await session.respond(to: "List all the states in the USA")
print(result.content)
}
Canvas Output
What I did
New file
Code
Canvas refreshes but nothing happens
Am I missing a step or setup here? Please help. Something so basic is not working I do not know what to do.
Running 40GPU, 16CPU MacBook Pro.. IOS26/Xcodebeta2/Tahoe allocated 8CPU, 48GB memory in Parallels VM.
Settings for Playgrounds in Xcode
Thank you for your help in advance.
Hi everyone,
I’m an AI engineer working on autonomous AI agents and exploring ways to integrate them into the Apple ecosystem, especially via Siri and Apple Intelligence.
I was impressed by Apple’s integration of ChatGPT and its privacy-first design, but I’m curious to know:
• Are there plans to support third-party LLMs?
• Could Siri or Apple Intelligence call external AI agents or allow extensions to plug in alternative models for reasoning, scheduling, or proactive suggestions?
I’m particularly interested in building event-driven, voice-triggered workflows where Apple Intelligence could act as a front-end for more complex autonomous systems (possibly local or cloud-based).
This kind of extensibility would open up incredible opportunities for personalized, privacy-friendly use cases — while aligning with Apple’s system architecture.
Is anything like this on the roadmap? Or is there a suggested way to prototype such integrations today?
Thanks in advance for any thoughts or pointers!
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
Tags:
SiriKit
Machine Learning
Apple Intelligence
I've implemented the imagePlaygroundSheet modifier in my app. It eventually all works but I've consistently noticed that the first time I present it, the sheet is totally blank. I then have to pull down to dismiss it (it doesn't even have a cancel button) and present it a second time and it loads content.
Just me? This is on 18.2 final, iPhone 16 Pro Max.
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
I have a Generable type with many elements. I am using a stream() to incrementally process the output (Generable.PartiallyGenerated?) content.
At the end, I want to pass the final version (not partially generated) to another function.
I cannot seem to find a good way to convert from a MyGenerable.PartiallyGenerated to a MyGenerable.
Am I missing some functionality in the APIs?
Hello,
I am testing the sample project provided here: Bringing advanced speech-to-text capabilities to your app.
On both macOS 26.0 beta and iOS 26.0 beta, the app crashes immediately on launch with a dyld "Symbol not found" error related to FoundationModels.framework.
It feels like this may be related to testing primarily on newer Apple Silicon devices, as I am seeing consistent crashes on an Intel MacBook and on an older iPhone device.
I would appreciate any insight, confirmation, or guidance on whether this is a known limitation or if there is a workaround. Is it planned to be resolved soon?
Environment
macOS:
Device: MacBook Pro (Intel)
Processor: 2 GHz Quad-Core Intel Core i5
Graphics: Intel Iris Plus Graphics 1536 MB
Memory: 16 GB 3733 MHz LPDDR4X
OS: macOS Tahoe Version 26.0 Beta (25A5338b)
iOS:
Device: iPhone 11
Model Number: MHDD3HN/A
OS: iOS 26.0
Xcode:
Version: 26.0 beta 3 (17A5276g)
Crash (macOS)
Abort signal received. Excerpt from crash dump:
dyld`__abort_with_payload:
0x7ff80e3ad4a0 <+0>: movl $0x2000209, %eax
0x7ff80e3ad4a5 <+5>: movq %rcx, %r10
0x7ff80e3ad4a8 <+8>: syscall
-> 0x7ff80e3ad4aa <+10>: jae 0x7ff80e3ad4b4
Console:
dyld[9819]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC
Referenced from: /Users/userx/Library/Developer/Xcode/DerivedData/SwiftTranscriptionSampleApp-*/Build/Products/Debug/SwiftTranscriptionSampleApp.app/Contents/MacOS/SwiftTranscriptionSampleApp.debug.dylib
Expected in: /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels
Crash (iOS)
Abort signal received. Excerpt from crash dump:
dyld`__abort_with_payload:
0x18f22b4b0 <+0>: mov x16, #0x209
0x18f22b4b4 <+4>: svc #0x80
-> 0x18f22b4b8 <+8>: b.lo 0x18f22b4d8
Console
dyld[2080]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC
Referenced from: /private/var/containers/Bundle/Application/.../SwiftTranscriptionSampleApp.app/SwiftTranscriptionSampleApp.debug.dylib
Expected in: /System/Library/Frameworks/FoundationModels.framework/FoundationModels
Question
Is this crash expected on Intel Macs and older iPhone models with the beta SDKs?
Is there an official statement on whether macOS 26.x releases support Intel, or it exists only until macOS 26.1?
Any suggested workarounds for testing this sample project on current hardware?
Is this a known limitation for the 26.0 beta, and if so, should we expect a fix in 26.0 or only in subsequent releases?
Attaching screenshots for reference.
Thank you in advance.
I have reinstalled everything including command line tools but the CreateML frameworks fail to install, I need the framework so that I can train my auto-categorzation model which predicts category based on descriptions. I need that framework because I want to use reviision 4.
please suggest advice on how do I proceed
I am attempting to install Tensorflow on my M1 and I seem to be unable to find the correct matching versions of jax, jaxlib and numpy to make it all work.
I am in Bash, because the default shell gave me issues.
I downgraded to python 3.10, because with 3.13, I could not do anything right.
Current actions:
bash-3.2$ python3.10 -m venv ~/venv-metal
bash-3.2$ python --version
Python 3.10.16
python3.10 -m venv ~/venv-metal
source ~/venv-metal/bin/activate
python -m pip install -U pip
python -m pip install tensorflow-macos
And here, I keep running tnto errors like:
(venv-metal):~$ pip install tensorflow-macos tensorflow-metal
ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none)
ERROR: No matching distribution found for tensorflow-macos
What is wrong here?
How can I fix that?
It seems like the system wants to use the x86 version of python ... which can't be right.
Hi,
I'm working with vision framework to detect barcodes. I tested both ean13 and data matrix detection and both are working fine except for the QuadrilateralProviding values in the returned BarcodeObservation. TopLeft, topRight, bottomRight and bottomLeft coordinates are rotated 90° counter clockwise (physical bottom left of data Matrix, the corner of the "L" is returned as the topLeft point in observation). The same behaviour is happening with EAN13 Barcode.
Did someone else experienced the same issue with orientation? Is it normal behaviour or should we expect a fix in next releases of the Vision Framework?
I am writing to inquire about content exclusion capabilities within Apple Intelligence, particularly regarding the use of configuration files such as .aiignore or .aiexclude—similar to what exists in other AI-assisted coding tools. These mechanisms are highly valuable in managing what content AI systems can access, especially in environments that involve sensitive code or proprietary frameworks.
I would appreciate it if anyone could clarify whether Apple Intelligence currently supports any exclusion configuration for AI-assisted features. If so, could you kindly provide documentation or guidance on how developers can implement these controls?
If not, Is there any plan to include such feature in future updates?
I'm interested in using Foundation Models to act as an AI support agent for our extensive in-app documentation. We have many pages of in-app documents, which the user can currently search, but it would be great to use Foundation Models to let the user get answers to arbitrary questions.
Is this possible with the current version of Foundation Models? It seems like the way to add new context to the model is with the instructions parameter on LanguageModelSession. As I understand it, the combined instructions and prompt need to consume less than 4096 tokens.
That definitely wouldn't be enough for the amount of documentation I want the agent to be able to refer to. Is there another way of doing this, maybe as a series of recursive queries? If there is a solution based on multiple queries, should I expect this to be fast enough for interactive use?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I am watching a few WWDC sessions on Foundation Model and its usage and it looks pretty cool.
I was wondering if it is possible to perform RAG on the user documents on the devices and entuallly on iCloud...
Let's say I have a lot of pages documents about me and I want the Foundation model to access those information on the documents to answer questions about me that can be retrieved from the documents.
How can this be done ?
Thanks
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
iOS 18 App Intents while supporting iOS 17
Hello,
I have an existing app that supports iOS 17. I already have three App Intents but would like to add some of the new iOS 18 app intents like ShowInAppSearchResultsIntent.
However, I am having a hard time using #available or @available to limit this ShowInAppSearchResultsIntent to iOS 18 only while still supporting iOS 17.
Obviously, the ShowInAppSearchResultsIntent needs to use @AssistantIntent which is iOS 18 only, so I mark that struct as @available(iOS 18, *). That works as expected. It is when I need to add this "SearchSnippetIntent" intent to the AppShortcutsProvider, that I begin to have trouble doing. See code below:
struct SnippetsShortcutsAppShortcutsProvider: AppShortcutsProvider {
@AppShortcutsBuilder
static var appShortcuts: [AppShortcut] {
//iOS 17+
AppShortcut(intent: SnippetsNewSnippetShortcutsAppIntent(), phrases: [
"Create a New Snippet in \(.applicationName) Studio",
], shortTitle: "New Snippet", systemImageName: "rectangle.fill.on.rectangle.angled.fill")
AppShortcut(intent: SnippetsNewLanguageShortcutsAppIntent(), phrases: [
"Create a New Language in \(.applicationName) Studio",
], shortTitle: "New Language", systemImageName: "curlybraces")
AppShortcut(intent: SnippetsNewTagShortcutsAppIntent(), phrases: [
"Create a New Tag in \(.applicationName) Studio",
], shortTitle: "New Tag", systemImageName: "tag.fill")
//iOS 18 Only
AppShortcut(intent: SearchSnippetIntent(), phrases: [
"Search \(.applicationName) Studio",
"Search \(.applicationName)"
], shortTitle: "Search", systemImageName: "magnifyingglass")
}
let shortcutTileColor: ShortcutTileColor = .blue
}
The iOS 18 Only AppShortcut shows the following error but none of the options seem to work. Maybe I am going about it the wrong way.
'SearchSnippetIntent' is only available in iOS 18 or newer
Add 'if #available' version check
Add @available attribute to enclosing static property
Add @available attribute to enclosing struct
Thanks in advance for your help.
I am excited to try Foundation Models during WWDC, but it doesn't work at all for me. When running on my iPad Pro M4 with iPadOS 26 seed 1, I get the following error even when running the simplest query:
let prompt = "How are you?"
let stream = session.streamResponse(to: prompt)
for try await partial in stream {
self.answer = partial
self.resultString = partial
}
In the Xcode console, I see the following error:
assetsUnavailable(FoundationModels.LanguageModelSession.GenerationError.Context(debugDescription: "Model is unavailable", underlyingErrors: []))
I have verified that Apple Intelligence is enabled on my iPad. Any tips on how can I get it working? I have also submitted this feedback: FB17896752
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Trying the Foundation Model framework and when I try to run several sessions in a loop, I'm getting a thrown error that I'm hitting a rate limit.
Are these rate limits documented? What's the best practice here?
I'm trying to run the models against new content downloaded from a web service where I might get ~200 items in a given download. They're relatively small but there can be that many that want to be processed in a loop.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models