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This work builds a GPU library that emulates SC matrix multiplication at scale, exposes stream lengths as first-class kernel arguments, and evaluates SC end-to-end on image classification, object detection and instance segmentation, class-conditional image generation, and visual world-model planning.
Results show that a compiler-defined symbolic interface turns native kernel synthesis into verified design-space exploration: compiler infrastructure preserves legality and correctness, while neural guidance improves workload-specific performance with lower search cost and complete coverage.
This work organizes agentic workflows in a taxonomy and presents its first architectural characterization with a production study at Microsoft Azure and a controlled study of open-source frameworks, showing that agentic execution is fragmented and heterogeneous.
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