fix(llm): snapshot vision encoder output before caching

[NorbertKlockiewicz]

Description

In any multimodal conversation with more than one image, the model starts describing earlier images as the most recently sent one on later turns.

VisionEncoder::encode caches the EValue returned by vision_encoder.execute() per image path. That tensor aliases the method's reusable output buffer, so the next execute() (the second image, or any later encode) overwrites the bytes behind every cached entry. On re-prefilled turns the prefiller then splices the latest image's embeddings into every image slot. The audio path already snapshots its encoder output for exactly this reason (see the AudioSlot comment in multimodal_prefiller.cpp); vision never got the same treatment.

The fix copies the encoder output into bytes owned by the cache entry immediately after execute() and serves cache hits from a tensor wrapping those owned bytes (unordered_map nodes are pointer-stable, so the blob stays valid).

The bug is backend-independent (the cache sits above the delegate), so XNNPACK/Vulkan multimodal models are affected the same way.

Introduces a breaking change?

• Yes
• No

Type of change

• Bug fix (change which fixes an issue)
• New feature (change which adds functionality)
• Documentation update (improves or adds clarity to existing documentation)
• Other (chores, tests, code style improvements etc.)

Tested on

• iOS
• Android

Testing instructions

1. Run the example LLM app with a multimodal model (e.g. Gemma 4 E2B multimodal) on the Multimodal LLM screen.
2. Send image A with "What's in this picture?" — answer is correct.
3. Send image B (different content) with the same question — answer is correct.
4. Ask "What was in the FIRST picture I sent?".

Before this fix, step 4 describes image B's content (both image slots receive B's embeddings on the re-prefilled turn). After the fix, the model correctly recalls image A.

Screenshots

N/A

Related issues

N/A

Checklist

• I have performed a self-review of my code
• I have commented my code, particularly in hard-to-understand areas
• I have updated the documentation accordingly
• My changes generate no new warnings

Additional notes