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?

Given that Foundation Models focus on native Swift APIs, is there any supported bridge for a WebKit-based app to access the Language Model Protocol?

When using the 'Summarize History' modifier, can we configure the summarization prompt to specifically preserve certain metadata like tool call IDs so that a resumed conversation can still reference previously executed app actions?

The Foundation Models framework is adding built-in OCR and barcode reader tools this year . If we implement a custom backend using the Language Model Protocol, can we return complex multi-modal objects (like bounding boxes or segmentation masks) back to the agentic flow, or is the protocol currently limited to text-based responses? For the 'Phone a Friend' pattern, is there a standard way to pass 'privacy-preserving embeddings' instead of raw text when calling a third-party model to maintain a higher level of user data protection?

Since agents in Xcode 27 can now interact with the accessibility tree and screenshots, can we provide 'developer hints' in our code to help the agent distinguish between decorative UI and critical interactive elements during automated testing? Can the Evaluations framework be used to 'score' the efficiency of an agent’s navigation path through the app, helping us identify where our App Intents might be creating confusing or redundant loops for Apple Intelligence?

While we now know about the continued-processing.gpu entitlement for background tasks, is there a similar NPU-specific entitlement or priority flag to ensure that an on-device foundation model isn't preempted by system-level Apple Intelligence features while the app is in the background?

Performance wise what are trade-offs when running an MLX-backed model on-device compared to using the system's AFM Core model? Also semiconnected: How do I use the 'model judge evaluator' to compare the accuracy of a custom LoRA adapter against the system's private cloud compute models?

In the WWDC26 AI Group Lab, it was mentioned as a 'spoiler alert' that the 'weight list applies only to Siri' and not to the Private Cloud Compute (PCC) language model . Could you clarify if there is a technical path for a developer’s custom adapter—running via the Language Model Protocol—to ever be added to this weight list to handle system-originated Siri requests?

Does the Foundation Models framework support notarized, non-App Store apps on macOS, and are there specific entitlements required to access on-device system models in that environment?

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

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?