Is there a supported way to differentiate the trigger source (i.e. Siri, Spotlight, or Shortcut) within the perform() method of an AppIntent?

My app has had shortcuts support since the Siri Intent days, so I have app intents with CustomIntentMigratedAppIntent to make sure old shortcuts continue working. Now I need to make further changes to one such shortcuts actions so I need to make a 2nd app intent to take the place of my older app intent that itself took the place of a siri intent. I need the old app intent to still exist for the sake of old shortcuts but I find that having isDiscoverable = false doesn't hide the 1st app intent so long as it has CustomIntentMigratedAppIntent. Is there any solution or do I have to either break old shortcuts or accept duplicate shortcuts actions to move my app intents forward?

Regarding the 'weight list' of Siri: can you all provide technical specifics on how a model qualifies for this list, and can a developer-supplied model/adapter ever handle requests that originate from the system-wide Siri interface?

Will the processing of an Intent result in an App Lifecycle change? How is compute time & network access managed when processing the intent? E.g., would I be able to make backend requests if needed?

Can I make functionality of my app available to Siri AI if they are NOT from one of the predefined App Schema domains? (This was already asked here: https://developer.apple.com/forums/thread/829586 ) Can I define my own App Schemas & App Schema Domains?

Hi all, This is a contrived example for an App Intent where I need my phrases to recognize three "regions" and a specific set of numbers associated with those regions. I am having trouble defining the AppEntity and associated AppShortcut phrases that works well with Siri, which is having trouble extracting numbers for some reason... Now the example: Imagine my app provides information surrounding Starbucks establishments on three major thoroughfares in NYC, such as: Broadway & 26, 40, 43, 54, 63, 75, 86, 95 Madison & 44, 50, 84, 96 Lexington & 43, 48, 55, 63, 67, 77, 85, 96 I need AppEntities, AppShortcuts, and friends that best work with Siri when launched with phrases such as: "Show 'example' for Madison and 50th in \(.applicationName)", "Show 'example' on 43rd and Broadway in \(.applicationName)", Long story short, Siri is picking up my phrases well, but then asking me to answer whether the value is 44, 50, 84, or 96, instead of just hearing that I said: "Madison and 50th" I've defined streets like this: public let nycStreetDisplayRepresentations: [DisplayRepresentation] = [ DisplayRepresentation(title: "26", synonyms: ["twenty six", "twenty sixth"]), DisplayRepresentation(title: "40", synonyms: ["forty", "fortieth"]), DisplayRepresentation(title: "43", synonyms: ["forty three", "forty third"]), DisplayRepresentation(title: "48", synonyms: ["forty eight", "forty eighth"]), DisplayRepresentation(title: "50", synonyms: ["fifty", "fiftieth"]), DisplayRepresentation(title: "54", synonyms: ["fifty four", "fifty fourth"]), DisplayRepresentation(title: "55", synonyms: ["fifty five", "fifty fifth", "five five"]), DisplayRepresentation(title: "63", synonyms: ["sixty three", "sixty third"]), DisplayRepresentation(title: "67", synonyms: ["sixty seven", "sixty seventh"]), DisplayRepresentation(title: "75", synonyms: ["seventy five", "seventy fifth"]), DisplayRepresentation(title: "77", synonyms: ["seventy seven", "seventy seventh", "seven seven"]), DisplayRepresentation(title: "84", synonyms: ["eighty four", "eighty fourth"]), DisplayRepresentation(title: "85", synonyms: ["eighty five", "eighty fifth"]), DisplayRepresentation(title: "86", synonyms: ["eighty six", "eighty sixth"]), DisplayRepresentation(title: "95", synonyms: ["ninety five", "ninety fifth"]), DisplayRepresentation(title: "96", synonyms: ["ninety six", "ninety sixth"]) ] And an entity like this: public struct MadisonThings: AppEntity, Identifiable { public let id: Int public var displayRepresentation: DisplayRepresentation { nycStreetDisplayRepresentations[id-1] } public static var typeDisplayRepresentation = TypeDisplayRepresentation( name: "Madison" , synonyms: ["Madison Ave"] ) public static var defaultQuery = MadisonTHingsQuery() } An EntityQuery: public struct MadisonThingsQuery: EntityQuery, EntityStringQuery { ...attempts to find synonyms in the phrase } Intent: public struct ShowMadisonThingsIntent: AppIntent { { @Parameter( title: "Street Number", description: "The street number (e.g., 5, 12, 44)" ) var streetNumber: MadisonThings // I have also tried just plain 'int' to no avail } And finally the shortcuts: static var appShortcuts: [AppShortcut] { return [ AppShortcut( intent: ShowMadisonThingsIntent(), phrases: [ "Show example for Madison and \(\.$streetNumber) in \(.applicationName)", "Show example on \(\.$streetNumber) in \(.applicationName)", ... This should be very simple, I think, and I've tried multiple ways of extracting numbers from my speech, but for some reason this is not working well at all. Thanks for your help!

Is there a supported way to 'pin' an app to a specific version of an on-device system model to prevent regression in agentic workflows & what is the typical update cadence for these weights?

For apps using the 'MLX distributor' or local adapters, are there any specialized background processing entitlements needed to ensure inference isn't killed by the OS during long-running tasks

When we specialize with preferredComputeUnitKind: .neuralEngine, the resolved options' allowedComputeUnitKinds return all three units, and sometimes a function we intend for ANE ends up on the GPU. We can't find any API that reports where a function actually ran, but system resource utilization shows a GPU spike. Is there a supported way to confirm the actual compute unit at runtime? And how does your prioritization work if we prefer running on the ANE rather than the GPU? Or, can we disallow certain compute units? How does this compute unit selection map with someone coming from CoreML where the desired compute units were honored?

In iOS 26, Core ML reliably submits inference requests targeting the Neural Engine in the background. In iOS 27 Seed 1, this capability is currently broken. Is Core AI expected to reliably submit inference requests targeting the Neural Engine in the background?

Will ahead of time compilation stay limited to A17 Pro and M1 class hardware? Docs indicate that AOT compilation is only supported for A17 Pro/M1 and newer. iOS 27 supports iPhone 11 (A13) and newer. AOT can't be used on A13-A16 which is a substantial fraction of active user devices in 2026 Is there a path to AOT on older Apple Silicon chips (A14 and newer) down the line? If not, I am afraid, the impact of AOT will be delayed by 3-4 years until it can be broadly leveraged.

Will ahead of time compilation stay limited to A17 Pro and M1 class hardware? Docs indicate that AOT compilation is only supported for A17 Pro/M1 and newer. iOS 27 supports iPhone 11 (A13) and newer. AOT can't be used on A13-A16 which is a substantial fraction of active user devices in 2026 Is there a path to AOT on older Apple Silicon chips (A14 and newer) down the line? If not, I am afraid, the impact of AOT will be delayed by 3-4 years until it can be broadly leveraged.

While response.usage provides token counts and reasoning signals, are there plans to expose per-token logprobs or confidence scores to help developers build more robust 'evaluators' for non-deterministic outputs?

We built a setup where a model split into an encoder and a decoder can run each part on a different backend, using our own component protocols. Is mixing Core AI and Core ML within a single inference pass something you would recommend, and what is the realistic cost at the boundary where we convert between MLMultiArray / MLTensor and NDArray? Is there a way to keep the encoder output resident on the GPU or ANE so it does not need a host round trip into the other backend?

No mention of Core ML at WWDC26... Shall we assume it was replaced by Core AI? What about Adapters?

What modern tools would you recommend to replace a drawing classification machine learning model I built years ago (with Turi Create) to recognize emojis trained on drawings users submit? I tried an image classifier with CreateML but training time is much longer and accuracy is worse. Does Apple have a solution for drawing classification? Is a ML model still the best solution, or any AI advancements good for this use case? Thanks!

When we specialize with preferredComputeUnitKind: .neuralEngine, the resolved options' allowedComputeUnitKinds return all three units, and sometimes a function we intend for ANE ends up on the GPU. We can't find any API that reports where a function actually ran, but system resource utilization shows a GPU spike. Is there a supported way to confirm the actual compute unit at runtime? And how does your prioritization work if we prefer running on the ANE rather than the GPU? Or, can we disallow certain compute units? How does this compute unit selection map with someone coming from CoreML where the desired compute units were honored?

The docs say we can delete the source .aimodel after a .persistent specialize and keep the bookmark to save space. But an OS update always invalidates the cache and bookmarks, so it looks like anyone who deleted the source has to re-download the whole model after every update. For large models, that's a lot of bandwidth and impacts the first-time experience after the update. Is that the intended trade-off, or does the cache hold enough to re-specialize itself? Does every minor OS bumps (27.1 -> 27.2) always invalidate cache when .persisted, or only major ones? Also, can the user delete a .persistent entry themselves through Settings or storage management, or only the app? We need to know whether our "model is ready" state can disappear without the app knowing.

The docs say we can delete the source .aimodel after a .persistent specialize and keep the bookmark to save space. But an OS update always invalidates the cache and bookmarks, so it looks like anyone who deleted the source has to re-download the whole model after every update. For large models, that's a lot of bandwidth and impacts the first-time experience after the update. Is that the intended trade-off, or does the cache hold enough to re-specialize itself? Does every minor OS bumps (27.1 -> 27.2) always invalidate cache when .persisted, or only major ones? Also, can the user delete a .persistent entry themselves through Settings or storage management, or only the app? We need to know whether our "model is ready" state can disappear without the app knowing.

AFM3 Core Advanced dynamically loads experts into DRAM* for Neural Engine execution. Is this capability accessible to third party via Core AI or Core ML APIs in iOS 27? Reference: https://machinelearning.apple.com/research/introducing-third-generation-of-apple-foundation-models