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

I'm using python 3.9.6, tensorflow 2.20.0, tensorflow-metal 1.2.0, and when I try to run import tensorflow as tf It gives Traceback (most recent call last): File "/Users/haoduoyu/Code/demo.py", line 1, in <module> import tensorflow as tf File "/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow/__init__.py", line 438, in <module> _ll.load_library(_plugin_dir) File "/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file) As long as I uninstall tensorflow-metal, nothing goes wrong. How can I fix this problem?

Hi everyone, I’m working on an iOS app that uses a Core ML model to run live image recognition. I’ve run into a persistent issue with the mlpackage not being turned into a swift class. This following error is in the code, and in carDetection.mlpackage, it says that model class has not been generated yet. The error in the code is as follows: What I’ve tried: Verified Target Membership is checked for carDetectionModel.mlpackage Confirmed the file is listed under Copy Bundle Resources (and removed from Compile Sources) Cleaned the build folder (Shift + Cmd + K) and rebuilt Renamed and re-added the .mlpackage file Restarted Xcode and re-added the file Logged bundle contents at runtime, but the .mlpackage still doesn’t appear The mlpackage is in Copy bundle resources, and is not in the compile sources. I just don't know why a swift class is not being generated for the mlpackage. Could someone please give me some guidance on what to do to resolve this issue? Sorry if my error is a bit naive, I'm pretty new to iOS app development

Hello everyone, I’m looking for guidance regarding my app review timeline, as things seem unusually delayed compared to previous submissions. My iOS app was rejected on November 19th due to AI-related policy questions. I immediately responded to the reviewer with detailed explanations covering: Model used (Gemini Flash 2.0 / 2.5 Lite) How the AI only generates neutral, non-directive reflective questions How the system prevents any diagnosis, therapy-like behavior or recommendations Crisis-handling limitations Safety safeguards at generation and UI level Internal red-team testing and results Data retention, privacy, and non-use of data for model training After sending the requested information, I resubmitted the build on November 19th at 14:40. Since then: November 20th (7:30) → Status changed to In Review. November 21st, 22nd, 23rd, 24th, 25th → No movement, still In Review. My open case on App Store Connect is still pending without updates. Because of the previous rejection, I expected a short delay, but this is now 5 days total and 3 business days with no progress, which feels longer than usual for my past submissions. I’m not sure whether: My app is in a secondary review queue due to the AI-related rejection, The reviewer is waiting for internal clarification, Or if something is stuck and needs to be escalated. I don’t want to resubmit a new build unless necessary, since that would restart the queue. Could someone from the community (or Apple, if possible) confirm whether this waiting time is normal after an AI-policy rejection? And is there anything I should do besides waiting — for example, contacting Developer Support again or requesting a follow-up? Thank you very much for your help. I appreciate any insight from others who have experienced similar delays.

I am running some experiments with WebGPU using the wgpu crate in rust. I have some Buffers already allocated in the GPU. Is it possible to use those already existing buffers directly as inputs to a predict call in CoreML? I want to prevent gpu to cpu download time as much as possible. Or are there any other ways to do something like this. Is this only possible using the latest Tensor object which came out with Metal 4 ?

Download the Foundation Models Adaptor Training Toolkit Hi, after I clicked on the download button, I was redirected to this page https://developer.apple.com and did not download the toolkit.

I'm on Tahoe 26.1 / M3 Macbook Air. I'm using VNDetectFaceRectanglesRequest as properly as possible, as in the minimal command line program attached below. For some reason, I always get: MLE5Engine is disabled through the configuration printed. I couldn't find any notes on developer docs saying that VNDetectFaceRectanglesRequest can not use the Apple Neural Engine. I'm assuming there is something wrong with my code however I wasn't able to find any remarks from documentation where it might be. I wasn't able to find the above error message online either. I would appreciate your help a lot and thank you in advance. The code below accesses the video from AVCaptureDevice.DeviceType.builtInWideAngleCamera. Currently it directly chooses the 0th format which has the largest resolution (Full HD on my M3 MBA) and "4:2:0" color "v" reduced color component spectrum encoding ("420v"). After accessing video, it performs a VNDetectFaceRectanglesRequest. It prints "VNDetectFaceRectanglesRequest completion Handler called" many times, then prints the error message above, then continues printing "VNDetectFaceRectanglesRequest completion Handler called" until the user quits it. To run it in Xcode, File > New project > Mac command line tool. Pasting the code below, then click on the root file > Targets > Signing & Capabilities > Hardened Runtime > Resource Access > Camera. A possible explanation could be that either Apple's internal CoreML code for this function works on GPU/CPU only or it doesn't accept 420v as supplied by the Macbook Air camera import AVKit import Vision var videoDataOutput: AVCaptureVideoDataOutput = AVCaptureVideoDataOutput() var detectionRequests: [VNDetectFaceRectanglesRequest]? var videoDataOutputQueue: DispatchQueue = DispatchQueue(label: "queue") class XYZ: /*NSViewController or NSObject*/NSObject, AVCaptureVideoDataOutputSampleBufferDelegate { func viewDidLoad() { //super.viewDidLoad() let session = AVCaptureSession() let inputDevice = try! self.configureFrontCamera(for: session) self.configureVideoDataOutput(for: inputDevice.device, resolution: inputDevice.resolution, captureSession: session) self.prepareVisionRequest() session.startRunning() } fileprivate func highestResolution420Format(for device: AVCaptureDevice) -> (format: AVCaptureDevice.Format, resolution: CGSize)? { let deviceFormat = device.formats[0] print(deviceFormat) let dims = CMVideoFormatDescriptionGetDimensions(deviceFormat.formatDescription) let resolution = CGSize(width: CGFloat(dims.width), height: CGFloat(dims.height)) return (deviceFormat, resolution) } fileprivate func configureFrontCamera(for captureSession: AVCaptureSession) throws -> (device: AVCaptureDevice, resolution: CGSize) { let deviceDiscoverySession = AVCaptureDevice.DiscoverySession(deviceTypes: [AVCaptureDevice.DeviceType.builtInWideAngleCamera], mediaType: .video, position: AVCaptureDevice.Position.unspecified) let device = deviceDiscoverySession.devices.first! let deviceInput = try! AVCaptureDeviceInput(device: device) captureSession.addInput(deviceInput) let highestResolution = self.highestResolution420Format(for: device)! try! device.lockForConfiguration() device.activeFormat = highestResolution.format device.unlockForConfiguration() return (device, highestResolution.resolution) } fileprivate func configureVideoDataOutput(for inputDevice: AVCaptureDevice, resolution: CGSize, captureSession: AVCaptureSession) { videoDataOutput.setSampleBufferDelegate(self, queue: videoDataOutputQueue) captureSession.addOutput(videoDataOutput) } fileprivate func prepareVisionRequest() { let faceDetectionRequest: VNDetectFaceRectanglesRequest = VNDetectFaceRectanglesRequest(completionHandler: { (request, error) in print("VNDetectFaceRectanglesRequest completion Handler called") }) // Start with detection detectionRequests = [faceDetectionRequest] } // MARK: AVCaptureVideoDataOutputSampleBufferDelegate // Handle delegate method callback on receiving a sample buffer. public func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) { var requestHandlerOptions: [VNImageOption: AnyObject] = [:] let cameraIntrinsicData = CMGetAttachment(sampleBuffer, key: kCMSampleBufferAttachmentKey_CameraIntrinsicMatrix, attachmentModeOut: nil) if cameraIntrinsicData != nil { requestHandlerOptions[VNImageOption.cameraIntrinsics] = cameraIntrinsicData } let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer)! // No tracking object detected, so perform initial detection let imageRequestHandler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, orientation: CGImagePropertyOrientation.up, options: requestHandlerOptions) try! imageRequestHandler.perform(detectionRequests!) } } let X = XYZ() X.viewDidLoad() sleep(9999999)

I am writing a custom package wrapping Foundation Models which provides a chain-of-thought with intermittent self-evaluation among other things. At first I was designing this package with the command line in mind, but after seeing how well it augments the models and makes them more intelligent I wanted to try and build a SwiftUI wrapper around the package. When I started I was using synchronous generation rather than streaming, but to give the best user experience (as I've seen in the WWDC sessions) it is necessary to provide constant feedback to the user that something is happening. I have created a super simplified example of my setup so it's easier to understand. First, there is the Reasoning conversation item, which can be converted to an XML representation which is then fed back into the model (I've found XML works best for structured input) public typealias ConversationContext = XMLDocument extension ConversationContext { public func toPlainText() -> String { return xmlString(options: [.nodePrettyPrint]) } } /// Represents a reasoning item in a conversation, which includes a title and reasoning content. /// Reasoning items are used to provide detailed explanations or justifications for certain decisions or responses within a conversation. @Generable(description: "A reasoning item in a conversation, containing content and a title.") struct ConversationReasoningItem: ConversationItem { @Guide(description: "The content of the reasoning item, which is your thinking process or explanation") public var reasoningContent: String @Guide(description: "A short summary of the reasoning content, digestible in an interface.") public var title: String @Guide(description: "Indicates whether reasoning is complete") public var done: Bool } extension ConversationReasoningItem: ConversationContextProvider { public func toContext() -> ConversationContext { // <ReasoningItem title="${title}"> // ${reasoningContent} // </ReasoningItem> let root = XMLElement(name: "ReasoningItem") root.addAttribute(XMLNode.attribute(withName: "title", stringValue: title) as! XMLNode) root.stringValue = reasoningContent return ConversationContext(rootElement: root) } } Then there is the generator, which creates a reasoning item from a user query and previously generated items: struct ReasoningItemGenerator { var instructions: String { """ <omitted for brevity> """ } func generate(from input: (String, [ConversationReasoningItem])) async throws -> sending LanguageModelSession.ResponseStream<ConversationReasoningItem> { let session = LanguageModelSession(instructions: instructions) // build the context for the reasoning item out of the user's query and the previous reasoning items let userQuery = "User's query: \(input.0)" let reasoningItemsText = input.1.map { $0.toContext().toPlainText() }.joined(separator: "\n") let context = userQuery + "\n" + reasoningItemsText let reasoningItemResponse = try await session.streamResponse( to: context, generating: ConversationReasoningItem.self) return reasoningItemResponse } } I'm not sure if returning LanguageModelSession.ResponseStream<ConversationReasoningItem> is the right move, I am just trying to imitate what session.streamResponse returns. Then there is the orchestrator, which I can't figure out. It receives the streamed ConversationReasoningItems from the Generator and is responsible for streaming those to SwiftUI later and also for evaluating each reasoning item after it is complete to see if it needs to be regenerated (to keep the model on-track). I want the users of the orchestrator to receive partially generated reasoning items as they are being generated by the generator. Later, when they finish, if the evaluation passes, the item is kept, but if it fails, the reasoning item should be removed from the stream before a new one is generated. So in-flight reasoning items should be outputted aggresively. I really am having trouble figuring this out so if someone with more knowledge about asynchronous stuff in Swift, or- even better- someone who has worked on the Foundation Models framework could point me in the right direction, that would be awesome!

Hello, I have to create an app in Swift that it scan NFC Identity card. It extract data and convert it to human readable data. I do it with below code import CoreNFC class NFCIdentityCardReader: NSObject , NFCTagReaderSessionDelegate { func tagReaderSessionDidBecomeActive(_ session: NFCTagReaderSession) { print("\(session.description)") } func tagReaderSession(_ session: NFCTagReaderSession, didInvalidateWithError error: any Error) { print("NFC Error: \(error.localizedDescription)") } var session: NFCTagReaderSession? func beginScanning() { guard NFCTagReaderSession.readingAvailable else { print("NFC is not supported on this device") return } session = NFCTagReaderSession(pollingOption: .iso14443, delegate: self, queue: nil) session?.alertMessage = "Hold your NFC identity card near the device." session?.begin() } func tagReaderSession(_ session: NFCTagReaderSession, didDetect tags: [NFCTag]) { guard let tag = tags.first else { session.invalidate(errorMessage: "No tag detected") return } session.connect(to: tag) { (error) in if let error = error { session.invalidate(errorMessage: "Connection error: \(error.localizedDescription)") return } switch tag { case .miFare(let miFareTag): self.readMiFareTag(miFareTag, session: session) case .iso7816(let iso7816Tag): self.readISO7816Tag(iso7816Tag, session: session) case .iso15693, .feliCa: session.invalidate(errorMessage: "Unsupported tag type") @unknown default: session.invalidate(errorMessage: "Unknown tag type") } } } private func readMiFareTag(_ tag: NFCMiFareTag, session: NFCTagReaderSession) { // Read from MiFare card, assuming it's formatted as an identity card let command: [UInt8] = [0x30, 0x04] // Example: Read command for block 4 let requestData = Data(command) tag.sendMiFareCommand(commandPacket: requestData) { (response, error) in if let error = error { session.invalidate(errorMessage: "Error reading MiFare: \(error.localizedDescription)") return } let readableData = String(data: response, encoding: .utf8) ?? response.map { String(format: "%02X", $0) }.joined() session.alertMessage = "ID Card Data: \(readableData)" session.invalidate() } } private func readISO7816Tag(_ tag: NFCISO7816Tag, session: NFCTagReaderSession) { let selectAppCommand = NFCISO7816APDU(instructionClass: 0x00, instructionCode: 0xA4, p1Parameter: 0x04, p2Parameter: 0x00, data: Data([0xA0, 0x00, 0x00, 0x02, 0x47, 0x10, 0x01]), expectedResponseLength: -1) tag.sendCommand(apdu: selectAppCommand) { (response, sw1, sw2, error) in if let error = error { session.invalidate(errorMessage: "Error reading ISO7816: \(error.localizedDescription)") return } let readableData = response.map { String(format: "%02X", $0) }.joined() session.alertMessage = "ID Card Data: \(readableData)" session.invalidate() } } } But I got null. I think that these data are encrypted. How can I convert them to readable data without MRZ, is it possible ? I need to get personal informations from Identity card via Core NFC. Thanks in advance. Best regards

I'm developing a tennis ball tracking feature using Vision Framework in Swift, specifically utilizing VNDetectedObjectObservation and VNTrackObjectRequest. Occasionally (but not always), I receive the following runtime error: Failed to perform SequenceRequest: Error Domain=com.apple.Vision Code=9 "Internal error: unexpected tracked object bounding box size" UserInfo={NSLocalizedDescription=Internal error: unexpected tracked object bounding box size} From my investigation, I suspect the issue arises when the bounding box from the initial observation (VNDetectedObjectObservation) is too small. However, Apple's documentation doesn't clearly define the minimum bounding box size that's considered valid by VNTrackObjectRequest. Could someone clarify: What is the minimum acceptable bounding box width and height (normalized) that Vision Framework's VNTrackObjectRequest expects? Is there any recommended practice or official guidance for bounding box size validation before creating a tracking request? This information would be extremely helpful to reliably avoid this internal error. Thank you!

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.

I'm really not familiar with ML, but I need a model that can enhance and denoise 4k video stream at 30fps. I have tried to search latest papers but they all have very complex structure, and I don't think I can convert them to mlmodel. So can anyone give me any recommandation for such models? If there is an existing mlmodel, that would be great!

After a few attempts I end up at the same spot as categorized here by ChatGPT - I get why it feels that way, Rob — honestly, you’ve walked right into the worst possible moment in Apple’s Core ML toolchain evolution. What you’re running into isn’t your fault; Apple really did rip out the ONNX front-end without offering a clean replacement yet. So every tutorial, every script, and even coremltools itself contradicts what’s actually possible right now. Let’s be brutally clear about the state of things (late 2025): Goal Supported by Apple today Practical workaround Convert ONNX → Core ML using coremltools 9 No. The ONNX importer was removed. Use the legacy coremltools 6.x + onnx-coreml stack in a separate Python 3.9/3.10 env. Convert PyTorch → Core ML directly Yes, but only for simple or traced models. T5 still fails because of dynamic control flow. None reliable without rewriting the model. Run ONNX models directly on iOS Partially (via MLC LLM / Metal Performance Shaders). Works, but that’s an entirely different runtime. So: you’re not crazy, and this isn’t wasted effort—you’ve actually proven that your model exports cleanly and that the only missing piece is Apple’s converter. The ONNX file you built is solid. You just need something that can translate it to Core ML. If your goal is simply “get it running on-device”, you still have two viable moves that aren’t rabbit holes: 1. Use MLC LLM / Metal runtime Skip Core ML altogether. Apple’s mlc-llm (and the “MLCChat” pipeline) can execute your ONNX or quantized model directly on the GPU. You keep full fidelity and avoid all these broken converters. 2. Use the legacy Core ML 6.x toolchain in a sandboxed Python 3.10 VM It actually works. You don’t have to downgrade your whole system—just run that specific environment once to emit the .mlpackage, then never touch it again. Nothing else will silently fix itself until Apple re-adds ONNX ingestion in Core ML 10 or a new converter.

I'm trying to use Apple's new Visual Intelligence API for recommending content through screenshot image search. The problem I encountered is that the SemanticContentDescriptor labels are either completely empty or super misleading, making it impossible to query for similar content on my app. Even the closest matching example was inaccurate, returning a single label ["cardigan"] for a Supreme T-Shirt. I see other apps using this API like Etsy for example, and I'm wondering if they're using the input pixel buffer to query for similar content rather than using the labels? If anyone has a similar experience or something that wasn't called out in the documentation please lmk! Thanks.

The WWDC25: Explore large language models on Apple silicon with MLX video talks about using your own data to fine-tune a large language model. But the video doesn't explain what kind of data can be used. The video just shows the command to use and how to point to the data folder. Can I use PDFs, Word documents, Markdown files to train the model? Are there any code examples on GitHub that demonstrate how to do this?

I followed below url for converting Llama-3.1-8B-Instruct model but always fails even i have 64GB of free space after downloading model from huggingface. https://machinelearning.apple.com/research/core-ml-on-device-llama Also tried with other models Llama-3.1-1B-Instruct & Llama-3.1-3B-Instruct models those are converted but while doing performance test in xcode fails for all compunits. Is there any source code to run llama models in ios app.

I am excited to share that I have developed a Metal kernel for Flash Attention that eliminates race conditions and fully leverages Apple Silicon’s shared memory and registers. This kernel can dramatically accelerate training of transformer-based models. Early benchmarks suggest that models which previously required months to train could see reductions to just a few hours on Apple hardware, while maintaining numerical stability and accuracy. I plan to make the code publicly available to enable the broader community to benefit. I would be happy to keep you updated on the latest developments and improvements as I continue testing and optimizing the kernel. I believe this work could provide valuable insights for Apple’s machine learning research and products.

I’m considering creating an ILMessageFilterExtension using a mini LLM/SLM to detect fraud and I’ve read it has strict memory limits yet I can’t find it in the documentation. What’s the set limit or any other constraints impacting the feasibility of running 100-500mb model?

Hello, I am studying macOS26 Apple Intelligence features. I have created a basic swift program with Xcode. This program is sending prompts to FoundationModels.LanguageModelSession. It works fine but this model is not trained for programming or code completion. Xcode has an AI code completion feature. It is called "Predictive Code completion model". So, there are multiple on-device models on macOS26 ? Are there others ? Is there a way for me to send prompts to this "Predictive Code completion model" from my program ? Thanks

Hello fellow developers, I'm the founder of a FinTech startup, Cent Capital (https://cent.capital), where we are building an AI-powered financial co-pilot. We're deeply exploring the Apple ecosystem to create a more proactive and ambient user experience. A core part of our vision is to use App Intents and the Shortcuts app to surface personalized financial insights without the user always needing to open our app. For example, suggesting a Shortcut like, "What's my spending in the 'Dining Out' category this month?" or having an App Intent proactively surface an insight like, "Your 'Subscriptions' budget is almost full." My question for the community is about the architectural and user experience best practices for this. How are you thinking about the balance between providing rich, actionable insights via Intents without being overly intrusive or "spammy" to the user? What are the best practices for designing the data model that backs these App Intents for a complex domain like personal finance? Are there specific performance or privacy considerations we should be aware of when surfacing potentially sensitive financial data through these system-level integrations? We believe this is the future of FinTech apps on iOS and would love to hear how other developers are thinking about this challenge. Thanks for your insights!