llama.cpp b10085 fixes Qwen3-VL vision embedding coordinate scaling

The latest release of llama.cpp (b10085) from ggml-org addresses a critical bug in the Qwen3-VL multimodal model's vision pipeline. The issue stemmed from a mismatch in how learned position embeddings were interpolated to the runtime patch grid. The default bilinear+antialias sampling (align_corners=False) diverged from the transformers reference implementation, which uses align_corners=True (matching torch.linspace(0, side-1, T)). This subtle discrepancy caused grounding coordinates to be scaled incorrectly around the image center, with errors becoming more pronounced on larger or non-square images.

The fix, contributed via pull request #25781 and referenced in issue #16880, ensures that visual grounding outputs—such as bounding boxes and spatial references—are now correctly aligned with the input image geometry. For developers building applications like visual question answering, document parsing, or robotic vision systems, this update is essential for accurate object localization. The release is available across all major platforms including macOS (Apple Silicon and Intel), Linux (CPU, Vulkan, ROCm, OpenVINO, SYCL), Windows (CPU, CUDA, Vulkan, OpenCL), and Android arm64. As llama.cpp remains one of the most popular open-source LLM inference engines (121k stars), this patch reinforces its reliability for production-grade multi-modal AI workloads.