Yuran’s PhD explores how intelligent systems can act as reasoning layers that mediate between human intent and low-level system signals in complex workflows. Across projects spanning embodied interaction, multimodal presentation systems, and ML system optimization, I derive empirical findings about how systems can encode interaction through form, couple feedback to user action at the pre-reflective level, fuse heterogeneous signals for intent inference, and expose autonomous reasoning to support human decision-making.
I synthesize these findings into DEXAS, a framework for human–agent collaborative reasoning, and instantiate it in PROMPTS, an agentic system that turns system traces into diagnostic insights and prunes large configuration search spaces through explainable proposals for optimizing large-scale LLM workloads. Together, this work establishes reasoning layers as a new systems abstraction that reduces the translation burden on experts and reconfigures their role toward high-level judgment.