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?

Hi, I'm trying to use the new RecognizeDocumentsRequest from the Vision Framework to read a receipt. It looks very promising by being able to read paragraphs, lines and detect data. So far it unfortunately seems to read every line on the receipt as a paragraph and when there is more space on one line it creates two paragraphs. Is there perhaps an Apple Engineer who knows if this is expected behaviour or if I should file a Feedback for this? Code setup: let request = RecognizeDocumentsRequest() let observations = try await request.perform(on: image) guard let document = observations.first?.document else { return } for paragraph in document.paragraphs { print(paragraph.transcript) for data in paragraph.detectedData { switch data.match.details { case .phoneNumber(let data): print("Phone: \(data)") case .postalAddress(let data): print("Postal: \(data)") case .calendarEvent(let data): print("Calendar: \(data)") case .moneyAmount(let data): print("Money: \(data)") case .measurement(let data): print("Measurement: \(data)") default: continue } } } See attached image as an example of a receipt I'd like to parse. The top 3 lines are the name, street, and postal code + city. These are all separate paragraphs. Checking on detectedData does see the street (2nd line) as PostalAddress, but not the complete address. Might that be a location thing since it's a Dutch address. And lower on the receipt it sees the block with "Pomp 1 95 Ongelood" and the things below also as separate paragraphs. First picking up the left side and after that the right side. So it's something like this: * Pomp 1 Volume Prijs € TOTAAL * BTW Netto 21.00 % 95 Ongelood 41,90 l 1.949/ 1 81.66 € 14.17 67.49

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

In macOS Tahoe 26.2 an RDMA capability was added for Thunderbolt-5 interfaces. This has been demonstrated to significantly decrease the latency and maintain bandwidth for "clustered" Apple Silicon devices with TB5. What is the ideal and the maximum RDMA burst width for transfers over RDMA-enabled Thunderbolt-5 interfaces?

Hi everyone, I’m exploring ideas around on-device analysis of user typing behavior on iPhone, and I’d love input from others who’ve worked in this area or thought about similar problems. Conceptually, I’m interested in things like: High-level sentiment or tone inferred from what a user types over time using ML-models Identifying a user’s most important or frequent topics over a recent window (e.g., “last week”) Aggregated insights rather than raw text (privacy-preserving summaries: e.g., your typo-rate by hour to infer highly efficient time slots or "take-a-break" warning typing errors increase) I understand the significant privacy restrictions around keyboard input on iOS, especially for third-party keyboards and system text fields. I’m not trying to bypass those constraints—rather, I’m curious about what’s realistically possible within Apple’s frameworks and policies. (For instance, Grammarly as a correction tool includes some information about tone) Questions I’m thinking through: Are there any recommended approaches for on-device text analysis that don’t rely on capturing raw keystrokes? Has anyone used NLP / Core ML / Natural Language successfully for similar summarization or sentiment tasks, scoped only to user-explicit input? For custom keyboards, what kinds of derived or transient signals (if any) are acceptable to process and summarize locally? Any design patterns that balance usefulness with Apple’s privacy expectations? If you’ve built something adjacent—journaling, writing analytics, well-being apps, etc.—I’d appreciate hearing what worked, what didn’t, and what Apple reviewers were comfortable with. Thanks in advance for any ideas or references 🙏

I was able to open a new project and play around with the Foundation Model, but when I dropped this class in a production app (with a lot of files) I'm running into Safety Guardrail errors for this very small prompt. Specifically it's "Safety guardrail was triggered after consecutive failures during streaming." Does it have something to do with the size of the app? I don't know what else to try to get it to work? import FoundationModels import Playgrounds @available(iOS 26.0, *) #Playground { Task { do { let session = LanguageModelSession() let prompt = "Write a short story about a talking cat." let response = try await session.respond(to: prompt) print(response) } catch { print("Error: \(error)") } } }

Context I trained a LoRA adapter for Apple’s on-device language model using the Foundation Models Adapter Training Toolkit v0.2.0 on macOS 26 beta 4. Although training completes successfully, loading the resulting .fmadapter package fails with: Adapter is not compatible with the current system base model. What I’ve Observed, Hard-coded Signature: In export/constants.py, the toolkit sets, BASE_SIGNATURE = "9799725ff8e851184037110b422d891ad3b92ec1" Metadata Injection: The export_fmadapter.py script writes this value into the adapter’s metadata: self_dict[MetadataKeys.BASE_SIGNATURE] = BASE_SIGNATURE Compatibility Check: At runtime, the Foundation Models framework compares the adapter’s baseModelSignature against the OS’s system model signature, and reports compatibleAdapterNotFound if they don’t match—without revealing the expected signature. Questions Signature Generation - What exactly does the toolkit hash to derive BASE_SIGNATURE? Is it a straight SHA-1 of base-model.pt, or is there an additional transformation? Recomputing for Beta 4 - Is there a way to locally compute the correct signature for the macOS 26 beta 4 system model? Toolkit Updates - Will Apple release Adapter Training Toolkit v0.3.0 with an updated BASE_SIGNATURE for beta 4, or is there an alternative workaround to generate it myself? Any guidance on how the Foundation Models framework derives and verifies the base model signature—or how to regenerate it for beta 4—would be greatly appreciated.

I want to use Foundation Models in a project, but I know my users will want to avoid environmentally intensive AI work in data centers. Does Foundation Models ever use Private Compute Cloud or any other kind of cloud-based AI system? I'd like to be able to assure my users that the LLM usage is relatively environmentally friendly. It would be great to be able to cite a specific Apple page explaining that Foundation Models work is always done locally. If there's any chance that work can be done in the cloud, is there a way to opt out of that?

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?

I used Yolo5-11 and while performing great detecting balls lets say 5-10ft away in 1920 resolution and even in 640 it really is taking toll on my app performance. When I use Create ML it outputs all in 415x which is probably the reason why it does not detect objects from far. What can I do to preserve some energy ? My model is used with about 1K pictures 200 each test and validate, and from close up and far.

Lately I am getting this error. GenerativeModelsAvailability.Parameters: Initialized with invalid language code: en-GB. Expected to receive two-letter ISO 639 code. e.g. 'zh' or 'en'. Falling back to: en Does anyone know what this is and how it can be resolved. The error does not crash the app

I am using macOS Tahoe on Xcode 26.

Hi For certain tasks, such as qualitative analysis or tagging, it is advisable to provide the AI with the option to respond with a joker / wild card answer when it encounters difficulties in tagging or scoring. For instance, you can include this slot in the prompt as follows: output must be "not data to score" when there isn't information to score. In the absence of these types of slots, AI trends to provide a solution even when there is insufficient information. Foundations Models are told to be prompted with simple prompts. I wonder: Is recommended keep this slot though adds verbose complexity? Is the best place the comment of a guided attribute? other tips? Another use case is when you want the AI to be tied to the information provided in the prompt and not take information from its data set. What is the best approach to this purpose? Thanks in advance for any suggestion.

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, I'm looking for the best way to use MLX models, particularly those I've fine-tuned, within a React Native application on iOS devices. Is there a recommended integration path or specific API for bridging MLX's capabilities to React Native for deployment on iPhones and iPads?

Hello, I was trying to test out Foundation Model however it says Model assets are unavailable. I got my MacBook M1 back in China when i was living there. is this due to region lock?

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?

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?

Environment MacOC 26 Xcode Version 26.0 beta 7 (17A5305k) simulator: iPhone 16 pro iOS: iOS 26 Problem NLContextualEmbedding.load() fails with the following error In simulator Failed to load embedding from MIL representation: filesystem error: in create_directories: Permission denied ["/var/db/com.apple.naturallanguaged/com.apple.e5rt.e5bundlecache"] filesystem error: in create_directories: Permission denied ["/var/db/com.apple.naturallanguaged/com.apple.e5rt.e5bundlecache"] Failed to load embedding model 'mul_Latn' - '5C45D94E-BAB4-4927-94B6-8B5745C46289' assetRequestFailed(Optional(Error Domain=NLNaturalLanguageErrorDomain Code=7 "Embedding model requires compilation" UserInfo={NSLocalizedDescription=Embedding model requires compilation})) in #Playground I'm new to this embedding model. Not sure if it's caused by my code or environment. Code snippet import Foundation import NaturalLanguage import Playgrounds #Playground { // Prefer initializing by script for broader coverage; returns NLContextualEmbedding? guard let embeddingModel = NLContextualEmbedding(script: .latin) else { print("Failed to create NLContextualEmbedding") return } print(embeddingModel.hasAvailableAssets) do { try embeddingModel.load() print("Model loaded") } catch { print("Failed to load model: \(error)") } }

Is anything configurable for LanguageModelSession.Guardrails besides the default? I'm prototyping a camping app, and it's constantly slamming into guardrail errors when I use the new foundation model interface. Any subjects relating to fishing, survival, etc. won't generate. For example the prompt "How can I kill deer ticks using a clothing treatment?" returns a generation error. The results that I get are great when it works, but so far the local model sessions are extremely unreliable.